bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Blurring and Sharpening Techniques Using Different Images Based on Heat Equations
Authors: Ali Ali Hasan , RASHEED Mohammed , SHIHAB Suha , RASHID Taha , Abed Hamed Saad Hussein
Published: October 2023

This paper is dedicated to show the practical part of the heat equation and its influence on images from real-life. Examples of one dimension and two dimensions are given in this paper to show how applying the forward and backward time steps on an image that is inserted as a pixel matrix in the well-known heat equation with certain parameters can make the blurring, noising, and sharpening effects. In addition, we are going to make a model for a heat diffusion in two directions x and y and assemble letters from components in matrices to apply our experiment on some medical and colorful images. Finally, we conclude that undesired details in an image can be removed by using the proposed equation.
Optimum Allocation of Graduation Projects: Survey and Proposed Solution
Authors: Gani Murtadha A. , Tayyeh Dr.Huda Kadhim , Hamid Dr. Rula A.
Published: October 2023

The final year project is an important part of obtaining a Graduation certificate in scientific and engineering universities. To ensure high quality in the completion of graduation projects, the project allocation process must be efficient and fair among students. Optimum allocation of graduation projects ensures a great experience for students and exceptional learning of new technologies from that projects, which may contribute to expanding students' future horizons. In this work, we review a wide range of research for the allocation project strategies that are used in various universities, in addition to the factors that have an effective impact on the selection of graduation projects by students. We also highlight the problems that these strategies tried to solve, in addition to those that they failed to solve. Finally, we present a modern idea to overcome too many problems that were presented in the literature discussion where the proposed system is based mainly on the student development level during the study stage.
A Review Of Skin Cancer Detection
Authors: Hussein Muhammed Kadhim , Stephan Jane J.
Published: October 2023

It can be said that the most common or common health disease is a skin disease. skin diseases are often determined by doctors' experience and sample results (skin biopsy), and it is certainly a time-consuming process. Therefore, there has become an urgent need for an automated system (computer) to identify and discover skin diseases through images with very high accuracy, with fewer doctors or experts in this purview. Identification and classification of the skin disease through the feature/s and characteristics that were taken from these images. Since skin diseases have very similar optical properties And therefore add a lot more challenges to choosing the useful feature/s of the image. This means that the accurate analysis of these skin diseases through images will have a good prognosis, short diagnostic time, and speed in diagnosis, and thus it will facilitate and cost-effective treatment. This paper provides an overview or study on the different methods and techniques for identifying and classifying skin diseases, which are traditional technology and technology based on deep learning.
Mathematical Modeling and Statistical prediction of the spread of the Covid-19 Epidemic in Iraq with use SIR, R0
Authors: Mohammad Gorgees Shaheed
Published: October 2023

In this research, a mathematical model was used to find epidemiological predictions for the coronavirus  epidemic in Iraq to control and prevent the epidemic. Where the data of the reported cases until Nov. 21, 2020 from the Iraqi Ministry of Health and the Iraqi Public Health Committee were used, where the parameters of the form were determined and based on determining the number of reported cases, the unreported cases are determined and then the model is used to project the epidemic forward with appropriate levels of Iraqi Public Health Authority interventions. The model's predictions underscore the importance of major public health interventions in controlling COVID-19 epidemics. Whereas, the research work strategy was adopted by Liu et al (2020). The simulation was performed with R.
An Accurate and Fast Computational Algorithm Based on Hybrid Methods
Authors: RASHEED Mohammed , SHIHAB Suha , RASHID Taha , Kamil Alkhazraji Mohammed Thamer
Published: October 2023

The numerical solutions of nonlinear equation for a single diode equivalent circuit of a solar cell are introduced. Four numerically algorithms, which include newton's; Predictor-Corrector Hally and Accelerated Predictor-Corrector Hally methods are described and compared in the present work. These algorithms are applied to calculate the voltage; current; power of a solar cell with the different values of load resistance programmed by Matlab language. The results showed that the proposed algorithm is the most efficient compare with other three algorithms.
Experimental Results for a Nonlinear Equation Using Improved Newton-Raphson Estimation Method
Authors: RASHEED Mohammed , SHIHAB Suha , RASHID Taha , Ali Yaseen Hadi
Published: October 2023

In this paper, two algorithms for solving the nonlinear equation of the solar cell within single diode model are proposed. These algorithms are plays an important and significant rule in the evaluation of this equation. The solution is examined with a various values of load resistance (1, 5) Ω at room temperature. The nonlinear equation of a solar cell is derived from the equivalent circuit of this cell. The obtained results reveal that the propose algorithm is appropriate one for solving such equations with the aid of Matlab language.
Numerical Solving for Nonlinear Problems Using Iterative Techniques
Authors: RASHEED Mohammed , SHIHAB Suha , RASHID Taha , Sabri Anwar AbdulJabbar
Published: October 2023

Three numerically algorithms are described and compared; newton's; Predictor-Corrector Hally and Accelerated Predictor-Corrector Hally methods have been applied in the present work. Algorithm development for solving the voltage of a solar cell based on it's a single diode equation; programmed using Matlab language. This example is given for induced the idea of this algorithm with the various values of load resistance. The results indicated the suggested algorithm is more efficient with the comparison with other two algorithms.
Partial Modular Space
Authors: Mohammed Abdulrahman A. , Shaakir Laith K.
Published: October 2023

In this paper we investigate new definitions called Partial Modular (P.M) and Convex Partial Modular (C.P.M) which are generalized of the definitions Modular and Convex Modular respectively . We can satisfy some results and properties of a partial modular (P.M) and we deduced some result in convex partial modular (C.P.M) . Finally we get a new study of relation between (P.M) and modular.
Results for Skew-Shape(9,7,3)/(1,0)
Authors: Abdul Rahman Rania Nazem , Hassan Haytham Razooki
Published: October 2023

In this paper, we  find the conditions for the sequence of characteristic  zero in the  case of the skew-shape . We also study this sequence as a diagram and prove that this sequence is complex  using  mapping Cone  and other concepts.
In The Solution of Nonlinear Equation
Authors: RASHEED Mohammed , SHIHAB Suha , Rashid Ahmed , Rashid Taha , Abed Hamed Saad Hussein , Abed Al-Farttoosi Ola Abdulelah
Published: October 2023

In this work, we present numerical method in order to solve non-linear equation of solar cell based on single diode model using two suitable approximations; Illinois Algorithm and Classic Chord techniques. Algorithms have discussed the tolerances of the values. It is founded that the new proposed method has the lesser number of evaluations than the other ones. Numerical examples are presented in order to test the efficiency, accuracy and the performance of this method. Numerical calculations observe that the proposed method is compatible with the famous existing methods and in this case gives the better results. The results obtained explain that the new suggested technique is more easy, accurate to use and efficient than other numerical methods are introduced.
Further Acceleration of Two-Point Bracketing Method for Determining the Voltages of Nonlinear Equation
Authors: RASHEED Mohammed , SHIHAB Suha , Rashid Ahmed , Rashid Taha , Abed Hamed Saad Hussein , Ridha AL-Sabbagh Yasir Ahmed
Published: October 2023

A general class of three point iterative algorithms for solving nonlinear equations based on single diode model of a PV cell is constructed in the present work. Three steps iterative methods for solving nonlinear equation of solar cell, Illinois Algorithm and Two-Point Bracketing algorithm are presented and analyzed. In addition; the absolute error values for all the algorithms have been discussed and compared. The new proposed method has the lesser number of iterations than the other ones has been founded. Numerical examples are contained to test and investigate the performance, accuracy and efficiency of these methods.
Use of Improved Algorithms for Solving Nonlinear Equation
Authors: RASHEED Mohammed , SHIHAB Suha , Rashid Ahmed , Rashid Taha , Abed Hamed Saad Hussein , Jasim AL-Kinani Mohammed Hussein
Published: October 2023

In this work, two two-step iterative methods are presented for solving nonlinear equations of PV cell in the type single diode style. The absolute error of these numerical methods is discussed too. Many numerical experiments are introduced and compared to other standard method such as Newton's method. The acquired results observe that the efficient, accuracy and fast of the proposed method.
Multistep Iterative Algorithms for Solving Nonlinear Equation
Authors: RASHEED Mohammed , SHIHAB Suha , Rashid Ahmed , Rashid Taha , Abed Hamed Saad Hussein , Hamed Alshebeeb Farazdack Fawzi
Published: October 2023

We present two new iterative methods, called the Secant and Dekker's Algorithms for solving nonlinear equations in this paper. These equations based on different values of load resistance R of a single diode scheme. We examined the effectiveness of these iterative methods by approximating the simple zeros of afford nonlinear equations. The approximate solutions of the numerical methods are accurate, stable, consistent and easy to use.
Hybrid Algorithms for Numerical Solution of Optoelectronics Applications
Authors: SHIHAB Suha , RASHEED Mohammed , Rashid Ahmed , Rashid Taha , Abed Hamed Saad Hussein , Ahmed Mifrijawee Raghad Hatem
Published: October 2023

In the present work, we approach and introduce some new iterative techniques free from second derivative of the given function. Per iteration, these new techniques run out five or four computations of the required function with it's first derivative. Absolute error of these techniques is also considered. Many examples numerically are chosen to indicate the accuracy and performance of these new techniques. These new techniques may be seen as an alternative to the popular methods.
Binomial Transform Technique For Solving Linear Difference Equations
Authors: Hussain Dr. Eman A. , Jasim Asmaa S.
Published: October 2023

Our goal in this paper is to find a new transformation technique for solving linear difference equations, as in the case of Z-transformation. And we were able to find the binomial transform, and this transform is one of the most common transformations. The linear difference equations can be solved by the binomial transformation.
Diagnose of Chronic Kidney Diseases by Using Naive Bayes Algorithm
Authors: Abd Noor S. , Abdullah Dhahir A.
Published: October 2023

Chronic kidney disease (CKD) develops gradually, usually after months or years when the kidneys lose function. In general, it may not be detected before it loses 25% of its functionality. Patients may begin to not recognize kidney failure because kidney failure may not give any symptoms at first. Treatment for kidney failure aims to control the causes and slow the progression of kidney failure. If the treatments are insufficient, the patient is in the end stage of kidney failure and the last treatment is dialysis or a kidney transplant. at this time. Therefore, it is necessary to make an early diagnosis to avoid reaching the stage of kidney failure. We conclude in this paper that the Naive Bayes algorithm is one of the best algorithms for diagnosing diseases with high accuracy of 99.24% and time of 0.003 seconds approximately because it is suitable for this kind of dataset.
DE-striping Augmented Images of Blood Cells using Deep Convolutional Neural Network
Authors: Shaker Atheel Sabih
Published: October 2023

The aim of this biomedical image processing-based research paper is to use augmented images of blood sample and Deep Convolutional Neural Network (DCNN) for the purpose of de-striping on Hyperion data to de-stripe on multicore platforms GPU. In order to reach our goal, we have started by analyzing the challenges associated with de-striping, Hyperion data and DCNN. A novel implementation pipeline of training, validating and evaluating stating from input an augmented blood image sample with the help analyzing the Hyperion data leading to the de-striping of augmented image blood sample by removing all the kernel black. What is clear is the high importance of applying the adequate pre-processing on Hyperion data because of low signal-to-noise ratio. By comparing the known layers of DCNN model for de-striping augmented images. The results obtained by applying the mentioned methods, it is revealed that all the higher stripes in an image as well as black color has been reduced and entirely associated with the Hyperion data alteration, and in contrast, the Hyperion imagery successfully corresponds to the de-striping of augmented image with an accuracy of 91.89% using DCNN model. The proposed DCNN is capable of reaching high accuracy within 150s after the launch of the evaluation phase and never reaches low accuracy. The pre-trained DCNN model approach would be an adequate solution considering de-striping as its high inference time is lower compared existing available methods which are not as efficient for de-striping.
SECURE RSA CRYPTOSYSTEM BASED ON MULTIPLE KEYS
Authors: Mahawash Al-Jubouri Ali Najam , Surayh Al-Janabi Dr. Rana Jumaa
Published: October 2023

Information and communication technology are spreading very rapidly in terms of information exchange over the Internet, and this information is vulnerable to threats by hackers. Information security is mainly achieved by using encryption techniques to protect it when it is transmitted over an unsecured channel. In this paper, a modified encryption system for the RSA algorithm is presented using a fixed encryption key size and divide that key into specific sections, to encrypt and decrypt blocks using multiple public and private keys. The encryption process can be done for each block by choosing different keys according to the random generator key (seed key) and encrypt each block with these different keys. Through the random arrangement of blocks and the properties of a modified cipher block in the RSA algorithm within the proposed model, to increase security at the expense of time, the use of large keys in the RSA algorithm is very slow since small RSA keys are vulnerable to factorization attacks. To overcome that problem, we increase complexity and use larger block sizes without sacrificing speed, and compare them with the original RSA algorithm. As a result, this method is more efficient, secured, and not easily breakable.
Trigonometrically Fitted Runge-Kutta Methods for the Numerical Solution of the Oscillatory Problems
Authors: Ghazal Zainab Khaled , Hussain Kasim Abbas
Published: October 2023

In this paper, two trigonometrically methods were established based on classical Runge-Kutta methods of the fourth and fifth-order respectively. The new methods will be applied for the numerical integration of oscillatory problems and have high effectiveness as the results demonstrate. Numerical results show the robustness and competence of the new methods compared to the well-known Runge-Kutta methods in the scientiﬁc literature.
Numerical Solving of Nonlinear Equation Using Iterative Algorithms
Authors: RASHEED Mohammed , Rashid Ahmed , Rashid Taha , Abed Hamed Saad Hussein , Manoochehr Noghanian Toroghi
Published: October 2023

In the given algorithm, we propose a development to the evaluations of Newton's numerical algorithm. Derivation of the standard method (Newton Raphson method) involves first derivative of the function. It is shown that the number of iterations of the new method is six and determined results support this technique. The results obtained show that the new proposed method is more accurate, easy to use, and efficient than other numerical methods are indicated.
Application of Numerical Analysis for Solving Nonlinear Equation
Authors: RASHEED Mohammed , Rashid Ahmed , Rashid Ahmed , Rashid Taha , Abed Hamed Saad Hussein , Abed AL-Farttoosi Ola Abdulelah
Published: October 2023

For the last years, the various of Newton's formula have become common iterative numerical techniques to realize approximate solutions to the zeros of nonlinear equations of PV cell (single diode) model foe electronic applications. These techniques do not necessitate the computation of second derivative of the functions but it need only first derivative of it's functions. In this paper, we introduce a new proposed method Dekker's Formula with five evaluations per iterations based on Accelerated Predictor-Corrector Halley's method. Numerical experiments produce that the new algorithm can determine with the standard Newton's algorithm.
Various Numerical Methods for Solving Nonlinear Equation
Authors: RASHEED Mohammed , Rashid Ahmed , Rashid Taha , Abed Hamed Saad Hussein , Manoochehr Noghanian Toroghi
Published: October 2023

In this work, two iterative methods is proposed and described here for solving nonlinear equation of PV cell device. The type of solar cell is single diode model based on its equivalent circuit with various values of load resistance R. These methods do not require second order of derivative and easy to use. The convergence of the proposed method do not discussed here. In order to found the accuracy and efficiency of the proposed and other method, the absolute error value have been calculated and compared. The advantages of the new proposed method can be investigated by comparing with the Improved Hornor-Newton method; and the results obtained indicate that the propose method is more accurate, efficient and easy to use than the second method.
Unusual Activity Detection in Surveillance Video Scene: Review
Authors: Mahdi Muthana S. , Mohammed Amer Jelwy , Jafer Mohamed M.
Published: October 2023

Abnormal activity may indicate threats and risks to others. An anomaly can be defined as something that deviates from what is expected, common, or normal. Because it is difficult to continuously monitor public spaces, intelligent video surveillance is necessary, which can monitor human actions in real-time and categorize them as ordinary or exceptional, as well as create an alarm. Human activities in public and sensitive regions such as bus stations, airports, railway stations, malls, banks, universities, car parks, roads, and other regions can be monitored using visual surveillance to prevent crime, theft, terrorism, vandalism, accidents, and other suspicious activities. ​This makes video surveillance a key to increasing public security. The main objective of event discovery is to discover the occurrence of events and categorize them into normal or abnormal actions. This discovery requires identifying and tracking objects and then learning what is going around those tracked objects. Recent research is based on one of two technologies: handcrafted features and deep learning models. Handmade features are based on extracting low-level features, and their strength is based on selecting the best features, that produce the best results. After the success of deep learning techniques for classifying images, the researchers examined the ability of deep learning techniques to detect, which bypasses the manual step of feature extraction and works directly with images. This paper presents a survey of both handmade and deep learning models to detect abnormal events.
Feature Level Combination for Face Recognition Based on Convolutional Neural Networks
Authors: Alrikabi Jamal M. , Alibraheemi Kadhim H.
Published: October 2023

Face detection and recognition systems have recently achieved encouraging results using deep learning, especially Convolutional Neural Network (CNN). Face Recognition (FR) systems have many challenges in unconstrained environments that decrease the accuracy; for overcoming these challenges, a deep learning-based features combination has been proposed. The scheme performs feature-level combination by applying two pre-trained GoogLeNet and VggNet-16 models as deep feature extractors. First, faces are detected and aligned using the Multi-Task Convolutional Neural Networks (MTCNN) face detector. The deep features are extracted from a face image using each individually pre-trained CNN. Second, features obtained from GoogLeNet and VggNet-16 models are combined using the serial-feature combination method. Finally, a classification task is performed using a multiclass Support Vector Machine (SVM) classifier. Experiments on the following datasets: VggFace2, LFW, Essex, and ORL, indicate the efficacy of the proposed system as the combination of the two pre-trained CNN models improves performance. The combination strategy, in particular, yields an accuracy of 95.33% to 99.29% on all datasets. The proposed system was compared to existing models in terms of the LFW, and ORL datasets, the findings showed that the proposed system outperformed most current models in terms of accuracy.
Finding the Critical Path Method for Fuzzy Network with Development Ranking Function
Authors: Mitlif Rasha Jalal , Mitlif Rasha Jalal , Sadiq Fatema Ahmad
Published: October 2023

We propose a development ranking function (RF) to solve project - scheduling problems (PSP) in a foggy environment. The development command works on fuzzy numbers (FN) and this is done by converting the fuzzy parameters to an explicit value and applying the critical path method (CPM) to obtain the solution described in the proposed algorithm. A clear definition of the time limit will aid in the successful implementation of CPM, there is often confusion regarding the length of the process leading to the development of a critical path method (CPM) system. The example and approach strongly suggest that the proposed method is efficient and gives us the critical path (CP) and identifies sensitive activities as well. The results show that the use of the development ranking function (DRF) is better, more efficient, and accurate than the other ranking function (RF) by calculating the hours of the project completion.
Cryptography model based on the principles of the geometric Series
Authors: Majeed Sundus Hatem
Published: October 2023

Given the rapid advancement of data innovation and the massive volume of data exchanged over the Internet, which needs protection from electronic theft and extortion, there are many encryption algorithms that developed to protect data. Development in the field of coding (Cryptography) has always been accompanied by a development in the field of decoding and Cryptanalysis. This led to the Weakness of many encryption algorithms. Therefore, the need to develop a new methods are necessary. In this paper, a method based on geometric series for encrypting data was proposed, where two keys are used, the amount of the incremental in sequence represents the first key; the number of terms in the sequence represents the second key. The proposed model has been tested using a number of appropriate attack techniques; the proposed model has proved its efficiency and strength against attacks several technique of attacks are applying on the propose model, the results proved that the strength of the proposed method. As a contribution, this paper using a sequence to concatenate the values of the sequence terms to hide the origin character ASCII code which adds fuzzy cryptography to guess the correct ASCII code.
A Survey paper on Lossy Audio Compression Methods
Authors: Abduljabbar Zinah S. , Ahmed Zainab J. , Al-Saleh Anwar H. H.
Published: October 2023

During the two last decades ago, audio compression becomes the topic of many types of research due to the importance of this field which reflecting on the storage capacity and the transmission requirement. The rapid development of the computer industry increases the demand for audio data with high quality and accordingly, there is great importance for the development of audio compression technologies, lossy and lossless are the two categories of compression. This paper aims to review the techniques of the lossy audio compression methods, summarize the importance and the uses of each method.
P-Wave Sonic Log Predictive Modeling with Optimal Artificial Neural Networks Topology
Authors: Alhelfia Labiba M. , Ali Hana M. , Ahmed Semaa H.
Published: October 2023

Given the financial challenges facing the oil and gas industry, the value of the information is considered relatively high; therefore, data science has been an alternative compensating tool. This study aimed to find an optimal neural network topology that provides an ideal data solution by studying neural network topology. Therefore, we trained different neural networks topologies in terms of the number of hidden neurons and layers. Volve oil field data is used in this study to predict the compressional sonic wave travel time. Optimal Neural Network topology found using five hidden layers and five hidden neurons while using a single layer with different numbers of hidden neurons was ineffective. The highest training and testing accuracy with a single hidden layer found 0.94 and 0.914, respectively. In contrast, it was found 0.947 and 0.934 with 50 hidden neurons and five hidden layers. Yet, increasing the number of hidden layers and hidden neurons is found to cause overfitting; therefore, only an optimal topology is a critical factor.
Detection of Unusual Activity in Surveillance Video Scenes Based on Deep Learning Strategies
Authors: Mahdi Muthana S. , Mohammed Amer Jelwy , Waedallah Abdulghafor Abdulghafour
Published: October 2023

In today's world, abnormal activity indicates threats and risks to others. An anomaly can be defined as something that deviates from what is expected, common, or normal. Because it is difficult to continuously monitor public spaces, intelligent video surveillance is necessary. When artificial intelligence, machine learning, and deep learning were introduced into the system, the technology had progressed much too far. Different methods are in place using the above combinations to help distinguish various suspicious activities from the live tracking of footage. Human behavior is the most unpredictable, and determining whether it is suspicious or normal is quite tough. In an academic setting, a deep learning Technique is utilized to detect normal or abnormal behavior and sends an alarm message to the appropriate authorities if suspicious activity is predicted. Monitoring is frequently carried out by extracting successive frames from a video. The framework is split into two sections. The features are calculated from video frames in the first phase, and the classifier predicts whether the class is suspicious or normal in the second part based on the obtained features. This paper proposes an effective method to design a system that automatically detects any unexpected or abnormal circumstance and alerts the appropriate authority and it can be used in both indoor and outdoor settings in an academic area. The proposed system was able to achieve an accuracy rate of 95.3 percent.
Image Compression Techniques: Literature Review
Authors: Al-jawaherry Marwa Adeeb , Hamid Saja Younis
Published: October 2023

With the development of modern communications technology, data compression is becoming more important to save space and reduce transmission costs. Because of this, various types and strategies of image compression were provided by several researchers, some of these studies were discussed in this review. The two main types of image compression are Lossless and lossy compression, with many methods for each of them. This research also described various lossless and lossy compression algorithms that were used by many researchers studied that reported in this literature. Lastly, certain conclusions have been provided based on the results of the conducted survey.
Enhanced PSO Based on Population Initialization and Exploration for the Permutation Flow Shop Scheduling Problem
Authors: Abdulhussein Azhar Y. , AL-Behadili Mohanad
Published: October 2023

In this paper, a hybrid PSO (NLPSO*) is adapted to improve the obtained local optimal solution for the Permutation Flow Shop Scheduling Problem (PFSP) with minimizing the makespan. In this method, an improved NEH heuristic called -NEH+ is used to generate a good initial population. Then the PSO is triggered, followed by Iterated Local Search (ILS) to increase the coverage of exploration and exploitation search in the solution space. Both of the -NEH+ and ILS  are simple and efficient algorithms.  A computational study is performed to show the efficiency of the proposed technique. Several of well-known PFSP instances of small, medium, and large sizes were used in this study. The experimental study shows that the NLPSO* algorithm is significantly efficient in reaching better local optimal solutions.
Memory Organization for Image Segmentation Based on Region Growing
Authors: Hilal Almiahi Osama Majeed
Published: October 2023

A solution is proposed for determining and organizing the size of the FIFO-stack in memory, required for load the coordinates of adjoining pixels in an image segmentation algorithm based on region growing. The FIFO-stack, organized on the idea of a ring multi-bit shift register, is considered, and the expression of memory calculations is performed through storing process in parallel registers. The conditions for maximum loading of the memory stack are formulated, for which an expression is obtained that allowing to accurately determining the required size of the FIFO-stack, which provides memory resources savings.
On Nb-Separation Axioms
Authors: Naief Anas Mohammed Saeed , Ali Haider Jebur
Published: October 2023

The main purpose of this paper is to express a new class of separation axioms based on open sets, known as Nb open, as well as to analyze and verify several essential ideas linked with this class.
Principally ⨁-g-supplemented modules
Authors: Obaid Miaad Makki , Ghawi Thaar Younis
Published: October 2023

In this paper, we defined and studied the idea of principally -g-supplemented modules as an advanced concept of -g-supplemented modules. Many properties, characterizations and examples of these modules are discussed. Also, a number of relations between these modules and other kinds of modules are examined in this work.
Some connections about sgrs ^⊕-modules
Authors: Kamil Narjis Mujtabah , Ghawi Thaar Younis
Published: October 2023

In this article, we introduced and investigated some relations between the concept of strongly generalized -radical supplemented module (for short, -module) and many other types of modules.
A Comparative Study for Skeleton Representation Methods Using Data Visualization
Authors: Zalaan Zainab H. , Najimb Safa A.
Published: October 2023

Data visualization is a technique used to see the unseen information, that will help humanity to discover important things. There are many methods to represent the datasets, but data visualization is the best because it can preserve the information. Skeleton is used to analyse the visualization, thus data visualization gives efficient results. Comparison among well-known methods is the goal of this paper. The conclusion of this paper showed all the comparative results will be important for any further study.  This study will use data visualization to discover novel dataset representations, which is the main goal of data visualization and can be useful in presenting necessary data.
Study the coupling agents of the common effect of adding high reactive mineral admixture meta-kaolin and super-plasticizer on the mechanical properties of lightweight concrete prepared by LECA aggregate
Authors: Husseina Hassan Hadi , Diaa Balqees M. , Frayyeh Qais j.
Published: October 2023

The purpose of this paper is to look at how employing both super plasticizers and highly reactive meta-kaolin additives affects the compressive strength regarding LECAC lightweight expanded clay aggregate concrete in addition to various concrete parameters. The coarse-grained aggregate utilized was lightweight clay with maximum size of 10mm; the best percentages regarding chemical and mineral additives have been partial substitution of 10% cement for meta-kaolin mineral additives and 0.6 L / 100 kg cement for Super plasticizers. The percentages that have been gained from improved compressive strength (8.6, 20, and 17.6) % for processing ages have been 7, 28, and 56, respectively, in comparison with indicated level mix when meta-kaolin mineral admixture was only added to it, whereas the common effect of the two additives was evident in percentages (25.2, 29.4, 28) % for treatment ages 7, 28, and 56, in comparison with the mixture that has been indicated via the plane. For a concrete mix comprising additives, the equilibrium density attained was 1798 kg/m3 (ACI 213-R-03, < 1840 kg/m3 for structural purposes). Furthermore, the water absorption regarding LECA concrete was lowered through adding Meta kaolin (9.6 and 7.7) % at (28 and 56) days of curing, respectively, while addition of super plasticizer produces a rise in water absorption, and above-mentioned ratios that have been reduced to (5.8 and 2.1) % at such ages.
Modern Drowsiness Detection in Deep Learning: A review
Authors: Jasim Sarah Saadoon , Abdul Hassan Alia Karim
Published: October 2023

Recent statistics show that drowsiness is now more of a factor in some car accidents than alcohol. So numerous monitoring systems have been developed to reduce, and accidents eliminate these events. Drowsiness detection systems are plentiful, but it isn't clear which one is the most effective. This review paper will address the following issues: firstly, determining the extracted features, whether behavioral-based, physiological-based, or vehicle-based, which are used to detect driver drowsiness; secondly, focusing on using deep learning to detect driver drowsiness. Finally, concluding from this study that the hybrid-based features should be used Because it gives strength in determining the drowsiness of the driver.
Comparison of LSTM And SVM For Classification of Eye Movements in EOG Signals
Authors: Abih Aya R. , Hayawi Mustafa J.
Published: October 2023

People with disabilities suffer from inability to communicate with their surroundings, so Human-Computer Interaction (HCI) technologies are used to have a means of communication for people with disabilities with their surroundings. HCI is an emerging technology in the disciplines of Artificial Intelligence and Biomedical Engineering. To power an external device, HCI technology uses several basic signals such as ECG, EMG, and EEG. Electrooculography (EOG) is a technique for measuring the potential difference between the cornea and the retina located between the front and back of the human eye, and the main application of EOG is to determine the directions of different eye movements. This study aims to assess eye movement for communication by persons with disabilities using electrocardiogram (EOG) data. In this study, the Supporting Vector Machine (SVM)  and Long- Short term memory (LSTM)  classification techniques was used and two types of features (statistical and time domain features) were used. Classification accuracy was 90.7% and 93.9% when using SVM with statistical domain and time domain features, respectively ,whereas  Classification accuracy was 90.1%  when using LSTM .
Almost Approximaitly Nearly Quasiprime Submodules
Authors: Ajeel Ali Sh. , Mohammadali Haibat K.
Published: October 2023

Let  be a commutative ring with identity and  be an -module. This study deals the concept of almost approximaitly nearly quasiprime submodules as a new generalization of quasiprime submodules, also generalizations of (prime, nearly quasiprime, nearly prime, approximaitly quasiprime and approximaitly prime) submodules. We give same basic properties, and characterizations of this concept. Furthermore, we study the behaviour of almost approximaitly nearly quasiprime submodules under -homomorphisms. Also, we explain the relationships between almost approximaitly nearly quasiprime submodules with the above concepts.
Principally ⨁-G-〖Rad〗_g-supplemented modules
Authors: Ghawi Thaar Younis , Kamil Narjis Mujtabah
Published: October 2023

In this paper, a new concept has been presented that is a stronger than a previous concept called -G--supplemented. The basic definition is in an  ring where an -module  is said to be principally -G--supplemented (shortly, -PG--supplemented) if any cyclic submodule  of  with , there exists a direct summand  of  such that  and . A set of properties and relations between previous modules and the given module has been dealt with simple examples illustrating those relations.
Asymptotic behavior of Eigenvalues and Eigenfunctions of T.Regge Fractional Problem
Authors: Faraj Jwamer Karwan Hama , Hilmi Hozan Dlshad
Published: October 2023

The asymptotic behavior of eigenvalues and eigenfunctions of T.Regge fractional boundary value problem has been shown, and we state and prove some theorems for many results, also some necessary definitions and results. In this paper, we look into a group of fractional boundary value problem equations involving fractional derivative fractional orders  and  with two boundary value conditions. We will which are important to state and prove those theorems; our primary findings are illustrated using examples.
An Approach to Compute the Multi-Objective Programming Problem Under Triangular Fuzzy Numbers
Authors: Mitlif Rasha Jalal
Published: October 2023

The most important one advantages of the operation research (OR) theory is displayed of this paper for tackling multi-objective function programming problems (MOFPP).  In this study, we present an approach for multi-objective fractional programming problems (MOFrPP) using fuzzy numbers (FN) coefficients in the objective functions (OF). The objective functions' (OF) parameters are all considered to be fuzzy triangular fuzzy numbers (FTrFN). This problem's multi-objective function (MOF) case was resolved by transforming the MOF to a linear programming problem (LnPP) that could be resolved using the simplex method (SM). The problem is being solved using algorithms. Then the value is compared with a result, which obtained by simplex method (SM). These techniques are illustrated with numerical examples (NE). This study shows the reliability of our methods and their utility to this specific set of multi-objective functions (MOF).
Statistical Analysis of Aluminum Doped Titanium Dioxide Using Solid State Method
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 nanoparticles were fabricated using the solid-state technique, then all of the aluminum materials were added in three ratios that are (0, 1, 2, and 3 (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.
Dom Tree as the base for webpage content extraction: Review
Authors: Rahim Hind Sabah , Sabir Aliea Salman , Flayh Nahla Abbas
Published: October 2023

Because of the fast advancement of internet technology in the last twenty years, which leads to a huge number of web pages that contain a massive amount of information in every domain, the volume of available information has been steadily expanding every minute, so the analyzing and extracting information from web pages is becoming increasingly crucial, add to that information in webpages in an unstructured or semi-structured format need to transform in a structured format.  

Since it is hard to collect the information manually, scientists have devised a variety of methods to help extract information from different domains in an automatic way. the main information in web pages is mixed with a significant amount of unrelated information (noise) like advertisements, boxes with links to relevant material, boxes with photos or other media, top and/or side navigation bars, animated commercials, etc., effect on the performance of information extraction and web content analysis technologies. to eliminate the noise by using the Document Object Model (DOM) that can easily reach every tag in the structure of the webpages to extract the information or delete the noise.

This article explores in-depth DOM tree-based approaches, such as HTML tags and the DOM tree, by reviewing works from 2011 to 2021 and comparing numerous elements comprehensively, including classifier methods, contribution, limitation, and evaluation metrics.
Survey: Crime Prediction using Machine Learning Approach
Authors: Khalaf Esraa Faisal , Taresh Ali Hasan
Published: October 2023

The presented studies have proven that machine learning algorithms The field of machine learning (ML) is expanding as more people realize how important it can be in a variety of crucial applications, including data mining, natural language processing, picture recognition, and expert systems. As it is well known that ML offers possible answers in all these domains and more, it is destined to be one of the pillars of our future civilization. This article presents an outline of the function of machine learning in prediction. algorithms have excelled in solving prediction and classification problems. Below We highlight the machine learning algorithms and techniques used in predicting crimes in particular, and the accuracy of the results obtained by each study or research. We see the challenges faced by a study or researcher who used machine learning algorithms and we hope with this paper, providing the researcher, in particular, with information covering the most important studies or research presented during the past five years to abbreviate the time of the researcher, and in the interest of his effort.
Review about applications of Remove haze from images
Authors: .Karoon Kholud A , Nemer Ainab N.
Published: October 2023

The problem of removing haze from images is a problem that researchers have been interested in, because of its importance in various fields in computer vision and image processing. The photos are usually taken in nature, like forests, cities, streets, etc., and are generally accepted in terrible weather conditions, especially fog. This is because the light is a critical factor in obtaining clear images, assuming the cameras' quality. The presence of various types of haze (smoke, fog, and dust) reduces the light rays that are reflected from the scenes to be photographed, so removing the haze was the researchers' primary concern. Moreover, many computerized devices use these cameras and imaging devices in various fields. Through this research, we will mention a number of those applications that use this biotechnology "dehazing."
Optimize Weight sharing for Aggregation Model in Federated Learning Environment of Brain Tumor classification
Authors: Mahlool Dhurgham Hassan , Abed Mohamed Hamzah
Published: October 2023

Clinical diagnosis and therapy of brain tumors are greatly aided by proper classification of the tumors.  Brain tumors can be diagnosed more quickly and accurately if radiologists use deep learning to help the specialist and doctors examine the enormous volume of brain MRI Images. Large datasets are required in training process, and whole of such data must be centralized for be handled by such techniques. It is sometimes impossible to collect and distribute patient data on a centralized data server because of medical data privacy regulations. In this paper, federated learning (FL) is proposed, in which data is non-shareable because of patient privacy issues. Using the FL approach, we have proposed two methods of aggregation; first, which concerns ranking the weight percentage of each client, and Second average weights method. and to evaluate the suggested model, we have compared the performance of the ranking weights percentage method with the average weights of proposed CNN and pre-training (VGG-16) in the FL environment in addition to SVM and VGG-16 . The experiments result was applied on two datasets, it shows our model accuracy result is very effective when using the ranking weight percentage method as compared with other methods, it achieves accuracy (98%) on datasets (BT_large-1c) and achieve (97.14%) on the dataset (BT-large-2c).
On Negative Logarithm Semigroup
Authors: Mohamed Ahmed Abdel Amir , Geem Methaq Hamza
Published: October 2023

In this work, we introduce certain type of semigroup, namely (Negative Logarithm semigroup) ,in the functional analytic study of differential equations. We construct a solution of the pde as the form:

Properties of this semigroup is studied.
Mining Distributed Data using Vertical Federated Learning Review
Authors: Yassen MManaaf Abdulredha , Muhammed Lamia AbedNoor
Published: October 2023

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

Federated Learning enables different repositories of data to learn a shared model collaboratively and at the same time keep the privacy of each one because of the increasing awareness of large firms compromising on data security and user privacy. To accomplish federated learning, three learning ways were suggested; horizontal federated learning, vertical federated learning, and transfer federated learning.

Vertical federated learning was adopted when data were spread among different parties. However, each one has different features from the others for identical objects. This paper is related to this type of federated learning.

To maximize model performance while maintaining the privacy of dispersed data, we'll create a framework based on vertical federated learning and suitable techniques.