Journal of Al-Qadisiyah for Computer Science and Mathematics

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.

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:
Capture.JPG
Properties of this semigroup is studied.

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 :

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

Capture3.JPG

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

Capture22.JPG

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 .

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.

Let Ⱳ be a nonzero unital left R -module and R be a commutative ring with nonzero identity. As a generalization of 2-Absorbing submodules, we provide the idea of Nearly Quasi Primary-2-Absorbing submodules in this article. As a proper submodule V of Ⱳ is called the Nearly Quasi Primary-2-Absorbing submodule of Ⱳ, if whenever rsx∈V for r,s∈R, x∈Ⱳ, implies that either rx∈Ⱳ-rad(V)+J(Ⱳ) or sx∈Ⱳ-rad(V)+J(Ⱳ) or rs∈√([V+J(Ⱳ)∶_R Ⱳ]). Several properties, characterizations and examples concerning this new notion are given.

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.

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 .

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.

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.

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)

In this research, titanium dioxide and Al nanoparticles were purchased from a Chinese company, then the copper materials were added in three ratios that are (0, 1, 2, 3, and 5) wt.%. The use of dry press method through a hydraulic press from one direction, using a mold of diameter of 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 copper added proportions and compared between them when adding the proportions for copper the ratio of 2 wt.% is the highest conductivity among the used ratios. For the mechanical properties, which included the hardness when adding percentage from copper 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.

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.

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.

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).

The demand for electronic -passport photo ( frontal facial) images has grown rapidly. It now extends to Electronic Government (E-Gov) applications such as social benefits driver's license, e-passport, and e-visa . With the COVID 19 (coronavirus disease ), facial (formal) images are becoming more widely used and spreading quickly, and are being used to verify an individual's identity, but unfortunately that comes with insignificant details of constant background which leads to huge byte consumption that affects storage space and transmission, where the optimal solution that aims to curtail data size using compression techniques that based on exploiting image redundancy(s) efficiently.

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.

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 .

In today\'s tumultuous and competitive business environment, executives are more motivated than ever to demonstrate their teams\' mettle by stellar results. The European Foundation for Quality Management (EFQM) has developed and verified the Excellence Model (EFQM) as the gold standard for measuring an organization\'s pursuit of excellence. An updated integrated strategy that incorporates fuzzy logic into the EFQM model is a powerful tool for boosting an organization\'s overall performance. This research utilizes the EFQM paradigm to suggest a novel and all-encompassing strategy for enhancing corporate performance. We looked at the specific situation of the Iraqi Oil Tanker Company to determine how doable the strategy actually is. With the use of the EFQM framework developed by the European Foundation for Quality Management, we have shown how useful this tool is for pinpointing both strengths and places for development. The RADAR Logical Method and Fuzzy-Based Strategy were utilized in this case study (EFQM). Matlab was used to apply the suggested technique for assessing the performance of the Iraqi Oil Tankers Company during a three-year period.

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.