Journal of Al-Qadisiyah for computer science and mathematics

Adaptive Features Selection Technique for Efficient Heart Disease Prediction

2023-02-17T18:49:37+00:00

Heart disease is a common disease that causes death and is difficult to detect manually. A more efficient classification model that relies on machine learning methods to achieve higher classification accuracy, attracts the attention of researchers to design an effective prediction model. Moreover, it plays an important role in the practical application of medical cardiology with the aim of early detection of heart diseases. In this paper, an efficient and accurate heart disease detection system is proposed based on the proposed adaptive feature selection technique using four machine learning methods: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). Two feature selection methods were used to design the proposed technique, mutual information (MI) and recursive feature elimination (RFE) to determine the optimal number of selected features that increase the performance of the classification models and reduce the time complexity of model implementation. The proposed technique was implemented on the two standard databases from the UCI machine learning repository: Cleveland heart disease and heart Statlog Cleveland. The best model was selected and saved as a prediction model using the cross-validation method. The results show that each data has a different number of features chosen according to the classifier model. For the first heart disease dataset, the best heart disease detection system Support Vector Machine-mutual information (SVM-MI) achieved the highest classification accuracy of approximately 96.755 compared to the other classifier models used. While the Random Forest-mutual information (RF-MI) model achieved an accuracy of 97.4% for the second data set. The proposed technique produced the highest prediction performance in terms of accuracy, f1 score, accuracy, and metric retrieval compared to the latest research in this field.

Designing the electronic payment system for the UOITC university using Zain Cash wallet

2023-02-17T18:49:45+00:00

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

2023-02-17T18:49:50+00:00

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.

Building an ontology for diagnosing Sidr tree diseases

2023-02-17T18:49:57+00:00

Sidr trees are among the important trees in Iraq, especially in the southern regions of the country, as in Basra Governorate. The Sidr crop contributes a good share of the economy in many regions. In addition, it is used in various fields such as medicine. Various pathogens affect the Sidr tree due to many diseases, which cause serious problems that weaken or stop the production of the plant, and may eventually cause the plant to die. Therefore, we find that direct work, whether at the individual level of farmers or institutions specialized in agricultural prevention, for the development of solutions to detect and diagnose diseases quickly, with high accuracy, and recommend treatment for Sidr diseases is necessary and inevitable. In this work, we build an ontology to represent information about Sidr tree diseases. The proposal of this ontology is to support agricultural practices and systems geared towards helping farmers in the early prediction of diseases from their morphological symptoms. The ontology was developed under Protégé 5.5.0 using the Web Ontology Language (OWL) format and defined Competency Questions, DL-Query, and SPARQL queries. It includes 217 classes, 13 object properties, 6 data properties, and 1762 axioms. Experiments conducted through a data set showed the effectiveness of ontology in diagnosing Sidr tree diseases using one or more observations of symptoms provided by farmers. As a contribution to this work, it presents the first ontology to recover knowledge about the diseases of the Sidr tree and the possibility of using this ontology in designing easy-to-use computerized systems (relying on semantic web technologies) that help the farmer in diagnosing diseases and suggesting appropriate treatment quickly, accurately and at a lower cost.

The Hierarchical Classification for The Rituals of Hajj Using Ontology

2023-02-17T18:50:03+00:00

In our dynamic world, knowledge is linked to data and information that already exists, and knowledge management can be shared and linked. In a broader scope, it can be used to structure ontology. Using ontology helps users understand structure easier and faster. In recent years, there has been a growing interest in developing and building an ontology for many fields.

However, relatively few works related to the religious aspect were conducted, especially regarding the pilgrimage and its rituals. This research paper was proposed given the importance of this field in Islam and the knowledge and exchange of everything related to it. Therefore, the proposed field was represented in ontology by showing the relationships between the categories that make up this field, which was organized hierarchically. When the relationship is already defined, the structure of the ontology can be modeled in the field of knowledge (pilgrimage and its rituals), and an appropriate methodology has been used for this work, which we will explain later.

This paper proposes to perform a hierarchical classification of pilgrimage rituals using an ontology file generated using the Protégé tool for modeling the ontology structure. This ontology structure will help share knowledge about Hajj and its rituals.

Age Invariant Face Recognition Model Based on Convolution Neural Network (CNN)

2023-02-17T18:50:09+00:00

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.

New random version of stability via fixed point method

2023-02-17T18:50:15+00:00

We studied the stability of the cubic functional equation:

3 ß( +3 ƴ)-ß(3 + ƴ)=12 [ß( + ƴ)+ß( - ƴ)]+80 ß(ƴ)-48 ß( ). (1.1)

via fixed point method in random normed space (ȐṄ −space).

Build Network Intrusion Detection System based on combination of Fractal Density Peak Clustering and Artificial Neural Network

2023-03-07T07:25:59+00:00

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.

Principally g-radical Supplemented Modules

2023-02-17T18:49:34+00:00

In this article we present a proper generalization of the class of g-radical supplemented modules. This class termed by P-g-radical supplemented. We determined it is structure. Several of these modules' characterizations, properties, and instances are described.

Numerical Method For Solving Fuzzy Singular Perturbation Problems With Initial Condition

2023-02-23T21:08:21+00:00

In this paper, We present a modified approach that makes use of the neuro-fuzzy system to solve fuzzy singular perturbation problems for ODEs with IC. The name of this modified approach is the modified neuro-fuzzy system method (MNFS). The foundation of this novel approach is to swap off each x in the input vector training. set = , a first-order polynomial which will be as = , . By using MNFS, it is possible to train the neural network outside of the initial and last point range by choosing training points based on the open interval (a, b). By resolving a few numerical cases and comparing the results to those calculated using different numerical techniques, we demonstrate this improved a technique and how neural networks demonstrate yield answers with accurate and strong generalization. The suggested approach is illustrated with a number of instances.

Modification Fuzzy Artificial Neural Networks For Solving Fuzzy Singular Perturbation Problems With Boundary Condition

2023-02-23T21:18:19+00:00

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

2023-03-02T20:21:26+00:00

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.