https://edusj.uomosul.edu.iq/index.php/csmj/issue/feed 2025-09-08T11:35:49+00:00 Professor Dr. Dhuha Basheer Abdullah [email protected] Open Journal Systems https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49399 2025-09-08T11:35:34+00:00 Ali mahmood alojedi [email protected] Abdulghafoor J. Salim -

<![CDATA[In this research, the reducible method or what is called the transformation method was generalized to the Stratonovich formula used to solve stochastic differential equations (SDEs), and the general formulas for the solutions and their theories were reached and the conditions necessary for reducing the stochastic differential equation were clarified by generalizing this method from the Ito integration formula to the Stratonovich integration formula and the transformation method between them, and these two integration formulas (Ito formula and Stratonovich formula) were applied to a group of diverse examples and the solutions were obtained and drawn (by the MATLAB program) and the results of the solutions for both methods were compared.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49408 2025-09-08T11:35:49+00:00 baraa salim ahmed [email protected] Salma Muslih M. Farris -

<![CDATA[In [1] , we introduce a new family of the Logistic map, namely the cubic logistic maps L= {L_ (x)=x^2 (1-x):>0,xR}. In this work we study the complex dynamics of this family i.e. L= {L_ (z)=z^2 (1-z): >0,zC}. That is we study the Fatou and Julia sets of this maps. In fact we give a whole description for these sets These two new types of logistics maps can address some of life's problems as shown in the introduction.We prove for any R, L_L is preservers R, critically finite, maps the negative x-axis into positive real line and has no any complex periodic point. Fatou set of these maps has no Siegel disk, Baker domain, and has no Wandering domain so they consist of parabolic domains and basins of attraction. Finally, we use escaping algorithm to construct the Fatou and Julia sets of our maps for various values of .]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49406 2025-09-08T11:35:46+00:00 Taha Huseein Ali [email protected] Mahammad Mahmoud Bazid -

<![CDATA[The Robust Partial Least Square Regression method is used to handle outliers and increase the explanation proportion, but it does not reduce the average of the mean square error. In this article, three methods are proposed to handle the problem of outliers, reduce the average of the mean square error, and increase the explanation proportion of the predictor and dependent variables. The first proposed method (Iteration) depends on identifying outliers by estimating the initial Partial Least Square Regression and then estimating outliers based on the residuals of those values to obtain the lowest mean square error, while the second and third proposed methods depend on a hybrid process between iteration and robust Partial Least Square Regression. The proposed and conventional methods were applied to estimate PLSR models on data Datasets for various ordinary patients in Iraq. The Dataset provides the patients Cell Blood Count test information that can be used to create a Hematology diagnosis/prediction system. Also, this Data was collected in 2022 from Al-Zahraa Al-Ahly Hospital. The proposed iterative method with higher efficiency provided 5 variables' importance in the projection score that explain the changes in HGB levels.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49405 2025-09-08T11:35:43+00:00 Aliaa Shaker Mahmoud [email protected] Mohammed Chachan Younis -

<![CDATA[Parallel processing is essential in machine learning to meet the computational requirements resulting from the complexity of algorithms and the size of the dataset, by taking advantage of the computational resources of parallel processing that can distribute computational operations across multiple processors. Which contributes to significant improvements in performance and time efficiency. This research demonstrated the impact of parallel processing on the performance and time efficiency of machine learning for pixel-based image classification techniques. The methodology includes pre-processing the Oxford IIIT Pet dataset, from which 4 cat images were selected. The performance of two supervised machine learning classifiers, decision tree, and random forest (10, 100, 500, and 1000 trees) were compared and implemented in two ways with and without parallel processing. The data is split in two ways: the first is by splitting the data by 70% for training data and 30% for testing data and the second is by cross-validation by splitting the data into four folds. The research aims to compare the accuracy and timely scales of machine learning models with and without parallel processing. The results showed a strong predictive power of the algorithms with an accuracy of 97.5%, while the training times were significantly reduced in parallel from 88.83 to 15.88 seconds for the RF100 model for image no. 2. This reflects the effectiveness of parallel processing in improving the performance of machine-learning models for pixel-based image classification. The proposed system was programmed using MATLAB 2021 language tools.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49404 2025-09-08T11:35:42+00:00 ADRIAN DAVID CHEOK [email protected] Timothy Adeyi -

<![CDATA[Smell is popularly believed to influence pain, even though several of these claims currently lack significant scientific or engineering support. This paper proposes a computing system for conducting smell vs pain experiments using a laboratory-built olfactometer together with cold pressor test (CPT) and pressure pain threshold (PPT) measurements. We conducted CPT and PPT experiments for three different types of smells: pleasant, sweet, and unpleasant. Pain threshold and pain tolerance of the participants were recorded and analyzed. These results concluded that sweet and pleasant smells can increase pain tolerance while unpleasant smells can decrease pain tolerance. In conclusion, applications of sweet and pleasant smells may be a beneficial addition to the management of pain symptoms. The results are relevant for workplaces where many people suffer from musculoskeletal pain, as well as in clinical settings where pain patients are treated. The key novelties of this paper are as follows: Firstly, we proposed a computer-controlled hardware system that can be used for smell vs pain experiments; secondly, we proposed a PPT and CPT combined testing method for smell vs pain experiments; and thirdly, we validated the effects reported for CPT in previous research using different odorants.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49402 2025-09-08T11:35:40+00:00 MOHAMMED JABER ALAWADI [email protected]

<![CDATA[Mobile ad hoc network is a creation of portable devices; they create a network with infrastructure on the fly that are ever changing. As in the previous network structure, each node performs both the action of a router and a host. Also, nodes cannot be fixed in the network; they can join or leave the network, and this increases the flexibility of the connectivity. There are routing protocols which are used for identification of efficient paths between the nodes in the network so as to seek the determination of the best routes between two nodes. This research show that routing is complex in MANETs and hence it demands the fine tuning of numerous routing protocols. We evaluate the effectiveness of these protocols by analyzing two primary metrics: are the average figures of throughput and average end to end delay. Simulation of this protocol was done using NS2 (Network Simulator) 2. 35, we investigate how well routing protocols fare in terms of different aspects including Size of the packets and number of nodes present]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49400 2025-09-08T11:35:35+00:00 Dilbar M.sharif Abdullah [email protected] Faraj Y. Ishak -

<![CDATA[we introduce a new predator-prey system and prove the existence, uniqueness, and stability of the proposed system. The main tools used in the study are the Picard approximation iteration and the principles of Ulam stability. Our results contribute to the understanding of dynamical behaviors in predator-prey interactions and provide a theoretical foundation for further studies in ecological modeling. The extends the Lotka-Volterra model to analyze the interaction between financial corruption and the population in society, offering insights into the complex dynamics of corruption and its societal consequences. Financial corruption, which has far-reaching effects on political, social, and economic structures, is examined through a modified version of the Lotka-Volterra system. By exploring the existence, uniqueness, and stability of solutions to the differential equations governing the system, this research provides a theoretical foundation for understanding how population segments and financial corruption levels influence each other over time. Key factors such as tipping points and societal consequences of corruption, including unrest and economic decline, are investigated.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49398 2025-09-08T11:35:32+00:00 Basim A. Hassan [email protected] Alaa Luqman Ibrahim - abd elhamid mehamdia -

<![CDATA[This research introduces and evaluates two enhanced conjugate gradient methods for unconstrained optimization, building upon the DaiLiao conjugacy condition and further refined through the application of Taylor series expansion. These novel methodologies were rigorously compared against the classical Hestenes-Stiefel (HS) method using a diverse suite of benchmark test functions. The numerical results obtained unequivocally demonstrate a significant improvement in computational efficiency achieved by the proposed methods. Notably, our enhanced methods consistently outperformed the HS method across several critical performance metrics, including a reduction in the number of iterations required for convergence, a decrease in the total number of function evaluations, and an overall faster computation time]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49397 2025-09-08T11:35:31+00:00 Heyam Abd Al-Majeed A.A.Hayawi [email protected]

<![CDATA[Outliers affect the accuracy of the estimated parameters of ARMA time series models which can be handled by the Hampel filter. In this article, wavelet shrinkage is proposed to handle outliers of ARMA models by using wavelet (Daubechies for order 4, Symlets for order 1, and Dmey) with a universal threshold method and applying a soft threshold. To compare the efficiency of the proposed method and the traditional method (Hampel filter), the mean square error, Akaike and Bayes information criteria were calculated for simulated and real data (The wind speed series data). The proposed method addresses the problem of outliers and provides estimated parameters for ARMA models with higher efficiency than the traditional method.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49393 2025-09-08T11:35:24+00:00 Abeer Mohamad Alshiha [email protected] Wai Lok Woo -

<![CDATA[In this research, the application of deep learning methods for the classification of human nail diseases using image analysis is investigated. The aim was to establish a non-invasive, automatic diagnosis tool for different nail conditions, utilizing deep convolutional neural networks (CNNs) for feature extraction. A total of 500 images of nails, divided into seven classes of diseases, were employed for training and testing. Feature extraction was performed using VGG16, ResNet50, and EfficientNetB0, and three machine learning classifiers, AdaBoost, LightGBM, and a Meta Classifier, were applied. The multi-classifier data classifier, the Meta Classifier, did better with 98.0% accuracy, 98.2% precision, 97.9% recall, and 98.0% F1 score when used in conjunction with EfficientNetB0. The study validates the efficacy of AI image diagnostics in non-invasive disease diagnosis, delivering a cost-effective and trustworthy method for early diagnosis, particularly in low-resource areas. The study verifies the accuracy of deep learning models, especially EfficientNetB0, for medical image examination, but extensive clinical validation and dataset acquisition are essential]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49392 2025-09-08T11:35:22+00:00 Mays Basil Jarjis [email protected] Hamsa Th. Chilmerane -

<![CDATA[In this research, a new conjugacy coefficient is derived for conjugate gradient method(C.G) and a new direction was obtained. In theory, this direction achieve sufficiently desent condition by using strong wolfe line search and global convergence is proved. When contrasted with the starnder HS (C.G) technique, the numerical performance of this approach is very remarkable. The Dolan-More performance profile was applied in order to carry out this determination. The amount of time that the central processing unit (CPU) spends, the number of iterations (NOI), and the number of function evaluations (NOF) all play a role in determining this profile. It was determined through the utilization of the Dolan-More profile that this was the case.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49391 2025-09-08T11:35:21+00:00 mohammed sami alghaloom [email protected]

<![CDATA[This study aims to enhance the security of Software-Defined Networks (SDN) byemploying deep learning techniques to detect cyber threats and mitigate attacks. A comprehensive data analysis was conducted, beginning with feature identification and dimensionality reduction using the Gain Information method to filter out redundant features, thereby improving model performance. Additionally, Min-Max normalization was applied to standardize feature ranges, and the SMOTE technique was utilized to balance the dataset and reduce the impact of underrepresented classes. The research compares the performance of three primary deep learning modelsCNN, LSTM, and ANNwith a newly proposed method designed to better differentiate between similar attack categories. The results demonstrate that deep learning models can effectively uncover hidden patterns in network traffic and accurately classify security threats, with the LSTM model particularly excelling in capturing temporal dependencies. While CNN and ANN models showed high accuracy in certain scenarios, they struggled to identify classes with fewer samples, necessitating the use of additional balancing techniques. Conversely, the proposed method showed promise in achieving a balance between accuracy and efficiency, suggesting that further refinement in feature engineering and advanced balancing strategies could enhance its performance. Overall, this study underscores the critical role of integrating deep learning with advanced preprocessing techniques in developing more reliable and effective intrusion detection systems for SDN environments.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49407 2025-09-08T11:35:47+00:00 Mohammed Sami Al-Hayali [email protected] Fawziya Mahmood Ramo -

<![CDATA[Cigarette smoking poses a major challenge to public health worldwide and has serious health consequences. Recent advances in deep learning, machine learning, Artificial Intelligence (AI), big data analytics, and computer vision have greatly enhanced smoking detection. These technologies enable the analysis of diverse datasets to identify patterns that indicate smoking behavior By enhancing the effectiveness of smart smoking detection systems And so we can better protect public health and reduce exposure to secondhand smoke in public places. An AI-based monitoring system is crucial to enhancing the fight against smoking in restricted areas by establishing a framework for identifying the locations of smoke detection systems across the city. This paper aims to shed light on the effectiveness of these smart systems in facilitating smoking cessation efforts and ensuring compliance with no smoking rules by reviewing previous studies on smoker detection and the algorithms used in those studies and their degree of effectiveness and efficiency in achieving the intended goal. They were examined, analyzed, classified, A comparison was made between research that used machine learning and research that used deep learning, and a comprehensive scientific comparison was made, with special attention paid to the data used to build the model. Furthermore, this paper will provide data on the results of indoor and outdoor smoker detection using smart algorithms, contributing valuable insights for future research in this area.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49403 2025-09-08T11:35:41+00:00 Sura Hasan Falih [email protected] Yaseen Hikmat Ismael -

<![CDATA[In recent years, technologies have brought about significant changes in education, with digitization becoming a key feature that can enhance the quality and effectiveness of the educational process. Traditional manual attendance and performance assessment systems face several issues, including time wastage, human error, and data loss. As a result, the adoption of technology-based systems that focus on RFID, Arduino, facial recognition, and machine learning technologies has led to increased efficiency and accuracy by automating these processes. Previous studies have explored a variety of approaches and techniques to achieve these goals, such as using RFID for automatic attendance recording, Arduino systems for automated testing, and machine learning algorithms for analyzing academic performance. However, these systems also face challenges, including financial constraints, privacy concerns, and difficulties in scalability. Therefore, this paper aims to provide a comprehensive review of these studies by examining their methodologies, results, and challenges.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49401 2025-09-08T11:35:37+00:00 Mohammed mansooor AL-Hammadi [email protected]

<![CDATA[In todays digital age, credit card fraud has become a serious issue, posing financial risks to individuals, businesses, and financial institutions alike. Detecting credit card fraud is crucial to limiting these risks and securing financial systems. This article presents an improved support vector machine (SVM)-based approach that integrates an advanced feature selection method for identifying fraudulent activities. By using a binary genetic algorithm and cross-entropy, our feature selection approach identifies key attributes and evaluates their relevance to the target variable. The SVM classification model then performs the final classification, with its hyperparameters optimized through the particle swarm optimization (PSO) technique. Experimental results on the Credit Card Fraud Detection dataset demonstrate the effectiveness of this method, achieving an impressive accuracy of 99.99%. By combining advanced feature selection with optimization techniques, this approach enhances the accuracy and efficiency of credit card fraud detection, offering a practical solution to combat fraud in financial systems.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49390 2025-09-08T11:35:15+00:00 Akhlas Tariq Hasan [email protected] Shayma Mustafa Mohi-Aldeen -

<![CDATA[Software bug prediction (SDP) techniques identify bugs in the early stages of the software development life cycle through a series of steps to produce reliable and high-quality software. Deep learning techniques are widely used in SDP, which can produce accurate and exceptional results in different fields.The study aims to systematically review models, techniques, datasets, and performance evaluation metrics to gain a complete understanding of current methodologies related to SDP, and the use of DL in software defect prediction research between 2019 and 2024. A comprehensive review of studies in this area was completed to answer the research questions and summarize the results from the initial investigations. 30 primary studies that passed the systematic review quality assessment of the studies were used. However, the six most common evaluation metrics used in SDP were confusion matrix, Scoar-1F, recall, precision, accuracy, and area under the curve (AUC). The top three DL algorithms used in building SDP models and used in predicting software bugs were convolutional neural network (CNN), long-short-term memory (LSTM), and bidirectional LSTM. We conclude that the application of deep learning in SDP remains a challenge, but it has the potential to achieve better prediction performance. Future research directions focus on improving these models and exploring their applications across diverse programming environments]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49396 2025-09-08T11:35:30+00:00 Rafal Ali Sameer [email protected]

<![CDATA[Video is a way to convey information on the internet which is more popular than the text. Video processing expanded with the using of videos in various fields and highly developing of communication technologies. The process of identifying the video shots is called video segmentation or video shot boundary detection. The aim of Shot boundary detection is to detect the shot boundaries and transitions between successive shots. Video shot boundary detection technique is one of the major research areas in video processing. There are various techniques for different types of videos proposed in this domain. This paper presents various techniques used for Shot boundary detection previously till now and their performance including their advantages, disadvantages, limitations, and future works.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49395 2025-09-08T11:35:28+00:00 Rana Mahammed Shafik [email protected] Nazar H. Shuker -

<![CDATA[This study explores the structure and properties of strongly Tri-nil clean rings. A ring is defined as strongly Tri-nil clean if every member in a ring can be expressed as the sum of a tripotent element and a nilpotent member, where these components commute. We provide the right singular ideal of a ring which is a nil ideal. We examine a ring with each element in R,^2 is zhou. we also found in these rings the char(R)=48, and every unit of order 4, Finally, we provide if R is a Tri-nil clean ring with 3N(R) if and only if every member of R is a sum of three tripotent and nilpotent that commute.]]>

2025-06-01T00:00:00+00:00 Copyright (c) https://edusj.uomosul.edu.iq/index.php/csmj/article/view/49394 2025-09-08T11:35:26+00:00 Olivia Khalil Oraha [email protected] Yusra Faisal Mohammad -

<![CDATA[Kinship verification is an automatic determining process of people relationships, if two or more individuals are in kin relation or not. Since the verifying of a kinship is the most challenging problem for many applications, where become beneficial in many fields such as investigation cases of missing people through war and natural disasters, biometric security and more. The DNA test is the most common surgical method to detect the kin relations between individuals of families but it does not work with some applications scenarios that need to real time results due to the DNA takes hours or days to give a result. Thus, with the progress of years the kinship verification entered the computer vision world to determining the relationships using machine learning (ML) algorithms such as deep learning, transfer learning techniques and others. Each part of the human body may have a significant embedded information (features) that extracted and analyzed for verification or recognition and classification for that individual. This paper presents a comprehensive review of the kinship verification methods used, datasets, features extraction and what the accuracy achieved.]]>

2025-06-01T00:00:00+00:00 Copyright (c)