Search Results for learning

Abstract

This study presents a machine learning-based framework for predicting and analyzing student academic performance using behavioral, psychological, demographic, and digital learning features. The dataset consists of 14,003 student records collected from higher education institutions in 2024 and obtained from Kaggle. It includes 16 variables covering study behaviors, engagement patterns, learning resources, psychological factors, and demographic characteristics. The target variable (response) was Final Grade, which was categorized into four performance levels: Low / Fail / Poor performance, below average performance, Good / Average performance, and Excellent / High performance, representing overall academic performance. Three machine learning algorithms XGBoost (Extreme Gradient Boosting), CatBoost (Categorical Boosting), and Support Vector Machine (SVM) were applied to classify student performance levels. The models were evaluated using confusion matrices, accuracy, F1-score, MCC, R², RMSE, MAE, and feature importance analysis. Results show that XGBoost achieved the highest performance (99.96% accuracy), followed by CatBoost (99.82%) and SVM (98.96%). Analysis of the importance analysis revealed that Exam Score is the most influential predictor across all models. The study highlights the effectiveness of advanced machine learning techniques in predicting academic success and supporting data-driven educational decision-making.

Abstract

Rapid evolution of the Internet of Things (IoT) has transformed the urban infrastructure because it is possible to interconnect systems that can improve transportation, healthcare, security, energy management, and public services. Implementation of IoT network in Iraqi cities faces numerous constraints including lack of infrastructural development, network saturation, unstable connections, poor cybersecurity and lack of resource management. These factors largely affect dependability and functionality of smart city applications. This paper examined how Artificial Intelligence (AI) methods applied to improve IoT network performance in Iraqi cities through effective data routing, predictive maintenance, anomaly detection, bandwidth optimization, and intelligent resource management. Machine learning, Deep Learning and reinforcement learning methods were commonly used methods to address the conditions of dynamic networks and maximize operational efficiency. The study aims at the application of AI techniques to strengthen current IoT systems to enhance scalability, minimize latency, enhance security, and energy efficiency. The research, based on analytical modeling and comparative performance analysis, shows that AI-based IoT systems significantly improve technological resilience of cities and assist Iraq in the development of smart cities. The results offer viable advice to policy makers, network engineers and urban planners who wish to develop modern cities in Iraq using intelligent technology.

Abstract

The study sought to activate the learning dimensions of the organization to become a (learning organization) capable of managing its strategic crises, so learning is the way that preserves its potential and strengthens its capabilities to be more rapid in reaction to successive changes and crises, so if organizations want to be effective in an environment dominated by uncertainty It has to rely on its ability to learn, and the availability of its dimensions will provide it with the ability to manage its strategic crises. Hence, the research problem started through a set of research implications that were:
- Is there a correlation relationship between the learning organization and strategic crisis management.?
- Is there an effect of the learning organization in strategic crisis management?
- Is there a contribution to the dimensions of the learning organization in strategic crisis management.?
The research assumed the existence of these dimensions in the researched organizations and how these dimensions affect the organization’s strategic crisis management, in order to achieve a flexible response to the turbulent changes of the crisis, and to achieve these objectives, a set of field-tested hypotheses were formulated by adopting the opinions of the members of the research sample, in order to accurately test the hypotheses and ensure their validity. Relying on a number of statistical methods appropriate for these relationships.
The research reached a set of Conclusions which confirmed its hypotheses to show the existence of a positive moral correlation as well as the existence of a moral impact of the learning organization in strategic crisis management. The educational dimensions of the organization as an explanatory variable have been identified 68% of the change in strategic crisis management as a responding variable. To present a set of recommendations aimed at activating the positive relations achieved and indicating the aspects that need to redraw their future paths in the researched organizations in particular and the corresponding organizations in general to confront the multiple crises, especially the (Covid-19) crisis.

Abstract

This study aimed to evaluate the financial sustainability of Iraqi industrial companies using machine learning models and to compare their performance with traditional statistical models. Five key financial variables were analyzed: Working Capital Ratio (WCR), Current Ratio (CR), Return on Equity (ROE), Return on Assets (ROA), and Asset Turnover (AT). A Financial Sustainability Index (FSI) was constructed to classify companies. The results showed that WCR and CR were the most influential variables in explaining financial sustainability, while AT had the least impact. All variables demonstrated statistically significant relationships with the FSI. Moreover, machine learning models—particularly XGBoost and Random Forest—outperformed the traditional logistic regression model in predictive accuracy, with variations in variable importance rankings across models. The findings highlight the importance of focusing on working capital management and liquidity improvement to enhance financial sustainability, as well as the use of machine learning tools in financial forecasting to support more accurate strategic decision-making. This study contributes by providing a modern analytical framework to support the financial sustainability of industrial companies, with the potential for future application in other sectors. Specifically, machine learning models improved predictive accuracy by approximately 9.4% over the traditional logistic regression model, while SHAP-based analysis confirmed that WCR and CR carry the greatest weight in predicting financial sustainability. Accordingly, the study recommends that industrial companies prioritize working capital and liquidity management, that financial analysts and investors adopt machine learning models such as XGBoost and Random Forest in financial sustainability forecasting, that more comprehensive sustainability indices combining financial and operational performance be developed, and that decision-makers invest in training on machine learning tools and SHAP interpretation to support more reliable, evidence-based financial decisions.

Abstract

This study aimed to evaluate the financial sustainability of Iraqi industrial companies using machine learning models and to compare their performance with traditional statistical models. Five key financial variables were analyzed: Working Capital Ratio (WCR), Current Ratio (CR), Return on Equity (ROE), Return on Assets (ROA), and Asset Turnover (AT). A Financial Sustainability Index (FSI) was constructed to classify companies. The results showed that WCR and CR were the most influential variables in explaining financial sustainability, while AT had the least impact. All variables demonstrated statistically significant relationships with the FSI. Moreover, machine learning models—particularly XGBoost and Random Forest—outperformed the traditional logistic regression model in predictive accuracy, with variations in variable importance rankings across models. The findings highlight the importance of focusing on working capital management and liquidity improvement to enhance financial sustainability, as well as the use of machine learning tools in financial forecasting to support more accurate strategic decision-making. This study contributes by providing a modern analytical framework to support the financial sustainability of industrial companies, with the potential for future application in other sectors. Specifically, machine learning models improved predictive accuracy by approximately 9.4% over the traditional logistic regression model, while SHAP-based analysis confirmed that WCR and CR carry the greatest weight in predicting financial sustainability. Accordingly, the study recommends that industrial companies prioritize working capital and liquidity management, that financial analysts and investors adopt machine learning models such as XGBoost and Random Forest in financial sustainability forecasting, that more comprehensive sustainability indices combining financial and operational performance be developed, and that decision-makers invest in training on machine learning tools and SHAP interpretation to support more reliable, evidence-based financial decisions.

Abstract

In light of the rapid digital transformation that has reshaped the educational landscape, smart learning technologies have emerged as one of the most significant contemporary trends that higher education institutions seek to adopt in order to enhance the quality of the educational process and improve both academic and administrative performance. The adoption of these technologies is no longer viewed as merely a developmental option; rather, it has become a strategic necessity driven by the demands of the contemporary educational environment and continuous technological advancements. Accordingly, this study aims to examine the adoption of smart learning technologies as the dependent variable through four dimensions: smart technological compatibility, performance expectancy, interaction, and institutional support, which collectively reflect the readiness and capability of educational institutions to effectively utilize modern technologies in supporting teaching and learning processes. Conversely, organizational mindfulness is examined as the independent variable through its five dimensions: preoccupation with failure, reluctance to simplify interpretations, sensitivity to operations, commitment to resilience, and deference to expertise. These dimensions enhance the institution's ability to anticipate organizational and technological changes and respond to them proactively and effectively. The significance of this study stems from the uniqueness of its empirical context within the Zakho Independent Administration, which is currently pursuing an ambitious vision for institutional development and educational modernization. The Technical College and Technical Institute in Zakho represent key pillars in supporting this transformation toward a more advanced and sustainable educational environment. The study adopted a descriptive-analytical research design and targeted a purposive sample of 82 faculty members and administrative leaders from the Technical College and Technical Institute in Zakho. To ensure the robustness and accuracy of the findings, Structural Equation Modeling (SEM) was employed using Smart PLS software to test the proposed hypotheses. The findings are expected to demonstrate that organizational mindfulness serves as a fundamental organizational capability that facilitates the successful adoption of smart learning technologies. Furthermore, the study provides a scientific framework that can support educational decision-makers in developing smart learning environments capable of meeting future educational challenges and promoting sustainable development within the region.

Abstract

Research aims to reveal nature of mediating role of organizational learning in relationship between strategic flexibility and high performance. Intellectual research problem was represented by main question: Does strategic flexibility have an impact on achieving high performance through mediating role of organizational learning? On basis of which sub-questions were raised through which appropriate solutions could be found. While the problem of field study of urgent need to respond to the repercussions of Corona pandemic was manifested through staff that have sufficient flexibility to achieve performance required of them to cross this crisis and sustain that performance through organizational learning of various kinds. Importance of research was evident from importance of the field of application, which needed to provide various solutions to advance and develop reality of health performance, as research was applied in Karbala Health Department on a random sample of (37) employees from its administrative and medical staff, based on a questionnaire form. Used a set of statistical methods to analyze data, including simple correlation coefficient (Pearson), analysis of ANOVA and test (Sobel) by using statistical program SPSS V.25. Research reached a set of conclusions, most important of which is existence of a direct correlation and a significant positive effect between research variables, in addition to presence of an indirect and significant effect of organizational learning in relationship between strategic flexibility and high performance. Based on conclusions of research, researchers were able to formulate a number of recommendations to improve performance levels and respond to Covid 19 crisis, most important of which is increasing interest in organizational learning at level of research community, as it represents real basis for enhancing and improving flexibility and performance of its human resources (administrative and medical staff) on one hand.

Abstract

The research aims to clarify the type of relationship between the independent variable, organizational sense and its sub-dimensions (context, language, cognitive frameworks, organizational identity), and the dependent variable, self-directed learning and its sub-dimensions (readiness for self-learning, self-control, planning, practice), The research was conducted at the Administrative Technical College in Baghdad on a sample of (100) faculty and staff members of the college. The questionnaire was distributed as the main tool for collecting information to the research sample, and (92) questionnaires were returned and deemed valid for statistical analysis, On the practical side, a number of statistical methods were used, including (reliability and significance tests, coefficients of variation and determination, arithmetic mean, and the outputs of the SPSS V.25 program). One of the most prominent results obtained practically was the existence of a statistically significant positive effect, The main effect of the independent variable, organizational sense, and its sub-dimensions, on the respondent variable, self-directed learning, and its sub-dimensions, are as follows: The most prominent research recommendations are: Working to strengthen informal communication channels, enhance language clarity, and improve work contexts and organizational policies to cultivate organizational awareness, while also fostering self-directed learning and emotional regulation.

Abstract

The integration of smart technologies into higher education is rapidly reshaping traditional teaching and learning environments. This study investigates differences in academic performance between students utilizing AI technologies and those who do not, spanning three educational levels: undergraduate, graduate, and postgraduate. Using a simulated dataset of 200 participants, the analysis employed independent sample t-tests, chi-square tests, regression models, correlation analyses, and visualizations such as scatter plots. The findings consistently demonstrate that students who adopt AI significantly outperform their peers, with notable statistical improvements across all groups. Additionally, factors like educational level and age were found to influence outcomes, with younger students and AI users showing greater consistency and higher achievement. Beyond presenting numerical data, the research contextualizes these results within modern digital learning theories, addresses challenges in technological integration, and offers practical recommendations for effectively incorporating AI tools into higher education frameworks.

Abstract

The aim of the research is to test the mediating role of the strategic capabilities in strengthening the relationship between strategic learning and digital business strategies by application in a sample of mobile telecommunications companies in the holy Karbala province, and in order to achieve this, Asia Cell, Zain Iraq, Korek Telecom, Al-Kafeel Security and Iraq Sale have been selected as an applied field. To search by distributing a questionnaire that included (64) respondents managers of the research sample companies, it included branch managers, department heads, divisional officials, administrative, technical and engineering units, as well as managing directors of private agencies. The simple correlation coefficient (Pearson) was used, and the simple regression equation as well as descriptive statistical methods (arithmetic mean, standard deviation) by adopting SPSS V.23 program, while the Sobel test was approved to prove the significance of the mean variable. And one of the most important conclusions that was reached is that the strategic capabilities have an effective influence in enhancing the relationship between strategic learning and the digital business strategy of companies, the research sample in light of competition in the Iraqi mobile telecommunications sector. The research was concluded with a number of recommendations, the most prominent of which is the need to employ the mediating role of the strategic potential in enhancing the influential relationship between strategic learning in order to adopt a digital business strategy within the rapidly growing telecommunications sector in line with the provision of new services that add value to the companies of the research sample as well as their employees across its geographically diverse branches.

Abstract

Breast cancer is one of the most prevalent and life-threatening diseases worldwide, making early and accurate diagnosis essential for improving patient outcomes. This study aims to evaluate the performance of two machine learning models, Extreme Gradient Boosting (XGBoost) and Gaussian Naive Bayes, for breast cancer classification using the Breast Cancer Wisconsin Diagnostic dataset. A comparative analysis was conducted based on several performance metrics, including Accuracy, Precision, Recall, F1-score, Specificity, Receiver Operating Characteristic (ROC) curve, Area Under the Curve (AUC), Balanced Accuracy, and Confusion Matrix. In addition, feature importance analysis was performed to identify the most influential variables contributing to the classification process. The experimental results demonstrated that both models achieved high classification performance. However, the Gaussian Naive Bayes model showed slightly superior performance, achieving an Accuracy of 96.5% and an AUC of 0.997, compared with 96.0% and 0.993, respectively, for the XGBoost model. Furthermore, the analysis revealed that features related to tumor morphology, particularly concave points mean, perimeter worst, and radius worst, were among the most influential predictors of breast cancer classification. The findings indicate that both machine learning models provide reliable and effective tools for supporting early breast cancer diagnosis, with Gaussian Naive Bayes demonstrating a slight advantage for the dataset used in this study. These results highlight the potential of machine learning techniques to enhance diagnostic accuracy and support clinical decision-making.

Abstract

Smart universities constitute a complex environment in which thousands of Internet of Things (IoT) devices are interconnected, rendering them an increasingly attractive target for sophisticated cyberattacks. This study aims to design an integrated framework combining Artificial Intelligence (AI) and IoT technologies for the early detection of cyberattacks within smart university environments, employing machine learning and deep learning algorithms — namely Random Forest and a hybrid CNN-LSTM model — to classify network traffic and detect anomalous patterns in real time. The study adopted a descriptive-analytical approach supported by a field questionnaire administered to a sample of 150 participants from IT and network security personnel across universities. Results revealed excellent questionnaire reliability (Cronbach's Alpha = 0.927) and a strong positive correlation (r = 0.674) between cybersecurity awareness and AI detection effectiveness, alongside the superiority of the proposed model over traditional systems by an accuracy margin of approximately 25 percentage points (96.3% vs. 71.2%). The study recommends adopting a five-layer architectural framework integrating IoT devices, data processing, AI inference, attack detection, and real-time response layers as a viable model for deployment in Arab smart universities.

Abstract

This study aims to compare and improve the methods of building investment portfolios for a sample of Iraqi banks listed on the Iraq Stock Exchange, by comparing traditional methods such as the Markowitz model with modern techniques based on machine learning. The Markowitz model is key to balancing return and risk across the medium-variance optimization framework, a traditional model that many financial institutions rely on. The study focused on exploring the extent to which machine learning techniques such as key component analysis (PCA), supporting vector machine (SVM), logistic regression, and random forest can improve the performance of the investment portfolios of these banks in a volatile environment such as the Iraq Stock Exchange. These techniques rely on processing and analyzing huge financial data to discover hidden patterns and relationships that help increase returns and reduce risk more effectively compared to traditional methods. The historical financial data related to the shares and assets of the banks of the research sample in the Iraq Stock Exchange was used to evaluate the performance of portfolios according to indicators such as expected return, variance, and Sharpe ratio. The study aims to provide innovative solutions that help banks make smarter and more effective investment decisions, commensurate with the local market conditions and the economic and political challenges they face.     

Abstract

Neuromarketing is an emerging field that combines neuroscience and marketing to better understand the factors influencing consumer decisions. Consumer purchasing behavior has long been a focus for marketers seeking to create compelling advertisements and increase engagement. It relies on objective tools to identify consumer responses through physiological and neurological measurements. This research aims to apply brain science to marketing research, specifically using electroencephalography (EEG) and eye-tracking to determine whether a consumer will purchase a product. “The association between physiological reactions and self-reported purchase intention to marketing cues was investigated by analysing physiological data from 210 participants. The link between the independent factors (product category and advertisement type) and the dependent variable (purchase intention) was investigated using descriptive, correlational, and regression methods. The results confirm that knowledge of neuromarketing significantly boosts practicality (β = 0.726, p < 0.001), which enhances marketing efforts (β = 0.611, p < 0.001) and social media communication effectiveness (β = 0.712, p < 0.001).” The study found that both frontal lobe alpha and beta wave activity (neural reactivity) and fixation duration and number of fixations (visual attention) were strong predictors of purchase intention, with fixation duration being the strongest predictor. Results suggest that emotional advertising elicited greater purchase intention than informational advertising, with the effects of emotion moderated by product category. This research presents evidence for a model that uses EEG and eye-tracking in conjunction to develop insights into consumer behavior. The paper provides an immediately applicable framework for marketers trying to revamp their advertising strategy.

Abstract

This research aims to provide a theoretical and applied framework for employing machine learning algorithms in management accounting and costing systems.

The research focuses on the importance of improving resource consumption monitoring, accurately tracking cost behavior, identifying unutilized energy, and supporting decision-making through historical data analysis to enhance the accuracy of production reports.

To achieve the research objective, a descriptive approach was adopted, drawing on available studies. A field study was also used, using a questionnaire to collect data from the research sample (the Electrical Cables and Wires Factory - Ur General Company).

The research also reached a number of conclusions, most notably that employing machine learning algorithms contributes to improving the prediction of quantitative resource consumption, which helps detect deviations and identify their potential causes, and enhances the accuracy and comprehensiveness of production reports.

The research concluded with a set of recommendations, most notably the need to establish an integrated data management system that includes operational data processing to provide real-time solutions and alternatives that contribute to supporting decision-making related to rationalizing resource consumption.

Abstract

E-learning is one of the important means that come as a result of the dangerous epidemic of Corona pandemic, which was applied to all Iraqi, Arab and international universities to avoid the spread of this dangerous epidemic that has assaulted humanity. Hence, it’s important to study the reality of applying E-learning, also study on situation of students and the use of statistical and studies Statistical to study this reality which will become realistic in use in the coming years

Among the statistics theories cluster analysis is used which is one of the important topics. A questionnaire from was distributed to student of the Ninavah Technical Institute (at the Northern Technical University). The Data included two axes, first one (exam performance) and the second (electronic exam). the sample number was 130 male and female students, and after debriefing the data, it was 11 damages questionnaires, The results were extracted and it was shown that the students’ ability to use the reality of E-learning increased and the skills and abilities to use modern education means and the internet increased.

Abstract

The current research aims to study the reality of the transition towards metaphysical education in Iraqi academic educational institutions, through analyzing the opportunities, challenges and strategic requirements associated with the application of metaphysical technologies in university education, based on the opinions of faculty members at the University of Kufa, and emphasizes the importance of research from the growing global interest in virtual reality and augmented reality technologies and immersive digital environments, and what they can provide great potential in developing the educational process and improving the quality of learning and academic interaction. The research was based on the descriptive-analytical approach for its suitability to the nature of the subject, and the questionnaire was used as the main tool for data collection, as it included three main axes, which were the opportunities for applying the Metaverse, the challenges of application, the strategic vision and readiness for transformation towards metaphysical education. The research community included faculty members and academic staff at the University of Kufa, and an intentional sample of teachers interested in digital transformation and modern educational technologies was selected. The research has reached a set of important results, most notably, there is a high positive trend towards the adoption of Metaverse technologies in university education, because of its ability to enhance interactive and immersive learning, develop students ' skills, improve the educational environment, as well as support academic innovation and expand learning opportunities without time or spatial restrictions .therefore, the results showed the existence of technical, financial, organizational and human challenges that may hinder the actual application of these technologies, including poor infrastructure, high hardware costs, lack of technical support, privacy concerns and legislation. The research stressed the importance of having a clear strategic vision for digital transformation, including the development of infrastructure, the provision of financial support, the qualification of academic cadres, the modernization of educational systems and legislation related to virtual environments, and the research ended with a set of recommendations, the most important of which is the need to adopt a comprehensive strategy for the transformation towards metaphysical education, strengthening partnerships with global technology companies, and the implementation of pilot projects before expanding the comprehensive application.

Abstract

This research aims to introduce electronic education and introduce the science of accounting as a science that can be taught electronically and to identify the most important challenges that face it in terms of addressing the technical and technical challenges and challenges facing the accounting student, the challenges facing the accounting teacher and the challenges facing the teaching of accounting electronically, Research’ importance in terms of scientific importance lies in addressing the scientific definitions of to the research related concepts topic. The practical importance lies in the possibility of seeing the opinions of students and professors of accounting science in the most important challenges facing the teaching of accounting electronically as the researcher follows In this research the descriptive and analytical approach in presenting the challenges facing e-learning for the science of accounting through the applied study and through the above, it is possible to arrive at contents that suggest the most important technical challenges represented in the weakness of the network and the means of scientific communication through the conference programs and the challenges facing the subject teacher from the difficulty of the effort Exerted in controlling and managing the huge number of students simultaneously, the difficulty of scientific explanation of the accounting subjet electronically, the difficulty of scientific recording of accounting electronically , the difficulty of conducting accounting operations electronically , the difficulty of conducting accounting operations electronically and the difficulty of providing electronic tools for use in accounting teaching and the smallness of computer screens, which causes Confusion in reading values ​​and financial numbers and commenting Albram C. Educational and internet disconnection during the lecture leads to your Lord in the delivery of accounting information. It is considered one of the most important obstacles in the field of teaching accounting electronically. The researcher also recommends educational institutions that contribute to the progress of the e-learning process after workshops and recovery courses.

Abstract

Given the swift digital changes occurring in the Banking industry, the purpose of this paper is to examine how well artificial intelligence systems can forecast and protect against future disasters.  By utilizing its skills in big data analytics, forecasting financial behavior, and more accurately and effectively managing risks, artificial intelligence (AI) is increasingly regarded as a crucial component in the development of banking systems and improving their operational efficiency.

 By enhancing client satisfaction, tailoring banking services to meet the demands of each individual, and cutting down on operational errors and administrative expenses, banks hope to gain a competitive edge by utilizing these technologies.  AI also helps to speed up credit decisions, make it possible to identify financial crime early, and create clever marketing plans based on forecasts of future market trends.

In order to ensure financial sustainability and achieve integration between digital transformation and the demands of banking innovation, studies show that the future of AI encompasses strategic, cultural, human, technological, and organizational dimensions in addition to technical ones.

 The paper also examined a number of anticipated long-term effects of AI applications, such as increased forecasting precision, lower operating expenses, better customer satisfaction, increased worker productivity, and assistance with investment choices.  The findings show that implementing AI applications in the banking sector is a strategic requirement to guarantee long-term growth and competitiveness in the digital era, not a technical luxury.

In order to enhance lending decisions and lower default risks, the paper also assesses how well a number of categorization algorithms work in assessing loan applicants' creditworthiness.  Using a dataset that represented the traits and financial activities of clients, seven machine learning techniques were used: Gradient Boosting, Random Forest, Extra Trees, Gaussian Naive Bayes, Logistic Regression, SVC-RBF, and KNN.

The paper used a database of 21 variables for loan applicants. Numerical variables included (age, income, credit score, debt-to-income ratio, and loan amount). Descriptive variables included (loan purpose, region, marital status, employer, educational level, and application channel). Binary variables included (whether or not the applicant had a history of default). These variables were used to predict the approval or rejection decision, with the dependent variable being represented by two values: 0 for rejection and 1 for approval.

The models were evaluated using the following six key performance indicators: Accuracy, Precision, Recall, F1 Score, Receiver Operating Characteristic Area Under the Curve (ROC AUC), and Brier Score.   The findings demonstrated that the Gradient Boosting algorithm performed best overall in both probability prediction quality and customer differentiation across different risk levels.  The Random Forest algorithm, which showed stability and balanced metrics, came next.  On the other hand, despite its moderate performance, Logistic Regression provided great interpretability, while the Gaussian Naive Bayes algorithm demonstrated high sensitivity in identifying high-risk customers.  In terms of overall accuracy and probability quality, some models—like SVC-RBF and KNN—performed worse.

Abstract

The current research presents the idea of ​​using deep learning tools and employing them in financial aspects due to their significant role and ability to explore unobservable aspects in light of financial models governed by a set of restrictions, conditions and linear relationships. On the other hand, the nature of financial data that tends to be non-linear and suffers from the missing of monthly closing prices, which imposes a state of data loss. All of this provides preference for deep learning models, including the neural network tool. The research aims to estimate financial returns in light of the capital asset pricing model CAPM as a financial model and neural networks as a deep learning tool in addition to the mask & padding tool to address the problem of missing data. The knowledge gap was determined by the inability of the capital asset pricing model to explore hidden and invisible aspects and overcome non-linear relationships. The research sample consisted of 42 organizations listed on the Iraq Stock Exchange for the period from 1/1/2021 to 31/12/2024 with 60 observations. The research concluded that the neural network tool is able to overcome the determinants in light of financial models and provide accurate estimates of returns are close to estimates under the capital asset pricing model.

Abstract

The primary objective of this study is to verify the effect of smart strategies on achieving organizational excellence at Zain Telecommunications Company. The researcher adopts the descriptive analytical approach in conducting the survey to achieve the study objectives. The researcher relied on two main tools in collecting data, namely the questionnaire and interviews, and distributed (80) questionnaires to the employees of Zain Company, but the valid for statistical analysis (73) and determine the sample in addition to identify the demographics of the sample (age - gender - educational qualification - years of service - job title) and verbal statements of their opinions about the smart strategies (continuous learning, technological integration, understanding the environment, strategic adaptation) as well as the organizational excellence (organizational excellence is measured by a set of variables, namely performance results, customer focus, internal processes, continuous improvement. The most important conclusions of the study were that strategic skills have a positive impact on achieving organizational excellence in Zain Telecommunications Company. Two-tailed test results on the correlation between smart strategies and organizational excellence show a strong positive correlation. A smart strategy will significantly impact organizational excellence. A high level of smart strategies will have a high level of organizational excellence and vice versa. Smart strategies are the driving force of change in the organization. Its positive impact on organizational excellence must be sufficiently emphasized and enhanced in the organization. The most important recommendations of the study are that Zain Company should work to create a collaborative multi-dimensional work environment to improve the performance of its employees and earn their satisfaction, and Zain Company should take into consideration the idea of sharing some responsibilities with its employees and providing them with the opportunity to make decisions, in order to improve skills and increase the sense of belonging, as well as putting more effort to apply digitization in all departments and workstations.

Abstract

The study aims to identify the conceptual framework of sustainable accounting and the role of Artificial Intelligence (AI) technologies in enhancing corporate reporting. Furthermore, it demonstrates the impact of adopting sustainability accounting on the financial reporting of a sample of five companies listed on the Iraq Stock Exchange from the year (2019) to the year (2022). The study relied on the descriptive analytical approach using the SPSS program. The main findings reveal the absence of a statistically significant relationship regarding the influence of disclosure in financial reports to apply sustainability standards on investors' decisions, as well as the absence of a significant relationship between the impact of sustainability accounting and the quality of financial reports. Integrating AI tools—such as machine learning and automated ESG analytics—can bridge these gaps by improving data accuracy, reducing reporting bias, and enhancing transparency. The study recommended a set of recommendations, the most important of which is obligating auditors specialized in the profession of auditing companies not to express an opinion on the validity of financial reports if they are devoid of sustainability accounting reports and supported AI-driven disclosure metrics.

Abstract

This research aims to measure the impact of using Internet of Things (IoT) and machine learning techniques on achieving the characteristics of accounting information and the extent to which this is reflected in the conceptual statement/8.This is based on the ability of artificial intelligence techniques to solve business problems and overcome obstacles facing companies by using highly advanced devices and applications, as it is a system capable on simulating human intelligence. According to the importance and problem of the research, and for the purpose of testing its hypotheses, a questionnaire was designed using Google Forms and distributed electronically to the research sample members, who were selected using a deliberate (intentional) sample consisting of employees from the departments of (financial affairs, auditing and internal control). (50) responses were obtained. Using the Statistical Package for the Social Sciences (SPSS), the results of the questionnaire were analyzed. According to the applied frame wall of the research, a set of conclusions were reached, the most important of which was that artificial intelligence techniques achieve the qualitative characteristics of accounting information, as stated in Conceptual statement/8. The researchers presented a set of The most prominent recommendations include the adoption by the Accounting Department of the Basrah Municipality Directorate of artificial intelligence techniques when performing various accounting tasks.

Abstract

The increasing proliferation of artificial intelligence tools in the university environment has led to noticeable changes in learning approaches and the completion of academic tasks, raising debate about their impact on the development of students’ critical and creative thinking skills. While some argue that these tools enhance knowledge production, others contend that they may reduce cognitive effort and independent analytical thinking. This study adopts a descriptive-analytical approach through a questionnaire administered to a random sample of university students from various academic disciplines. The aim is to measure the level of use of artificial intelligence tools and examine the nature of their impact on critical and creative thinking skills, including analysis, inference, problem-solving, and brainstorming. The study also seeks to answer a set of research questions related to the extent of students’ use of AI tools, their contribution to improving higher-order thinking skills, and the presence of statistically significant differences based on selected demographic and academic variables. The findings are expected to contribute to developing a scientific perspective on how artificial intelligence tools can be effectively and responsibly integrated into higher education in a balanced manner that enhances students’ thinking skills without negatively affecting their cognitive independence. Moreover, the results may assist policymakers and universities in designing educational policies that promote the conscious and responsible use of artificial intelligence technologies in the learning process.

Abstract

This paper reviews the growing role of AI in accelerating the discovery and design of inorganic nanomaterials for sustainable development through a six-stage pipeline: Data → AI → Prediction → Design → Fabrication → Application. Using a critical-analytical approach supported by quantitative metrics (99.99% correlation for TiO₂ band gap, screening of >10 million oxides, >2 compounds/day at A-Lab), it systematically compares ML, DL, and GNN techniques, alongside DFT-based high-throughput screening, inverse design, active learning, and self-driving laboratories. Based on 30 peer-reviewed references (2013–2026) selected using PRISMA guidelines from Scopus, Web of Science, and Google Scholar.

Abstract

This research aims to analyze the impact of data-driven marketing on enhancing the strategic position of organizations through the mediating role of digital organizational culture. The research's significance stems from the increasing reliance of organizations on data and digital analytics in formulating marketing decisions and developing their competitive strategies. This contributes to enhancing their ability to adapt to the changing business environment and achieve strategic superiority. The research adopted a descriptive-analytical approach, and data was collected through a questionnaire designed based on relevant scientific literature. The questionnaire was distributed to a sample of employees in private banks in Baghdad, with a sample size of 384 employees. The data were analyzed using a range of appropriate statistical methods to test the correlation and influence relationships between the research variables. The SPSS version 30 and AMOS version 29 software programs were used. The results showed a significant impact of data-driven marketing on enhancing the strategic position of private banks. The results also indicated that digital organizational culture plays a crucial mediating role in strengthening the relationship between data-driven marketing and strategic position. These findings suggest that banks adopting data-driven marketing strategies, along with establishing a digital organizational culture, contribute to this strategic position. A supportive organizational structure for digital transformation, which effectively contributes to improving its competitiveness and enhancing its strategic position in the banking market, the research recommended the need to enhance the use of data analytics in the marketing activities of banks, develop the digital infrastructure, as well as establish a digital organizational culture that encourages innovation and continuous learning in order to support the achievement of the strategic position of banking organizations.

Abstract

    The research aimed to test and know the role that digital leadership plays in individual work performance within the framework of the interactive role of knowledge sharing behavior, as the research problem centers on scientists’ belief that the COVID-19 virus may be with us for decades or more, and data and information were collected about the selected sample through the questionnaire. Which is considered a main tool in this research, as the sample included members of the teaching staff in a number of universities and private colleges in the Middle Euphrates region, amounting to (312) individuals. In order to support the relationship between these variables from a theoretical perspective, the study relied on measuring the digital leadership variable on a scale that includes: Five dimensions (visionary leadership, learning culture in the digital age, excellence in professional practice, systemic improvement, and digital citizenship), and as for the knowledge sharing behavior variable, a scale was adopted that includes four dimensions (written contributions, organizational communications, personal interactions, and communities of practice) While the study adopted a scale that includes four dimensions to measure individual work performance (task performance, contextual performance, adaptive performance, and adverse work behavior), the descriptive analytical approach was adopted in presenting and interpreting the information for the research, and for the purpose of analyzing and processing the data statistically, a group of Descriptive statistical measures, Pearson correlation coefficient, structural equation modeling, and modified analysis. These methods were used through the statistical program (Spss.25, Amos.25). The most prominent conclusions are that the knowledge sharing behavior in some universities and private colleges, the study sample, is The field aspect would enhance the impact of digital leadership on individual work performance, and this means the presence of knowledge sharing behavior in the field alongside digital leadership that would contribute to individual work performance in these private universities.

Abstract

This research aims to analyze the role of integrating artificial intelligence (AI) technologies in promoting digital transformation in the banking sector. This is of great importance due to its significant impact on monetary policy. Through the application of digital technologies, banks and financial institutions can greatly improve their procedures, leading to changes in banking liquidity. Digital transformation can contribute to increased cash flows and improved liquidity management, thus affecting the central bank's ability to implement monetary policy effectively. Thanks to big data analytics and AI, banks can better identify economic needs, influencing interest rate decisions. Therefore, it can be said that digital transformation plays a significant role in shaping monetary policy and opens the door to significant improvements in the management of fiscal and monetary policies and creative thinking skills. Traditional reliance on auditing methods does not keep pace with rapid technological developments, and auditors' weak adoption of creative thinking methods in addressing audit problems negatively affects the quality of audits and the accuracy of their results. The importance of this research lies in its aim to provide a comprehensive framework that combines modern technological tools and creative approaches to digital work, thereby contributing to the development of critical practices and enhancing the quality of its results according to standards of efficiency and effectiveness. The advanced statistical software Smart Pals and appropriate statistical methods were used to test the hypotheses. The results showed a statistically significant effect of using artificial intelligence technologies (expert systems, machine learning, continuous auditing, natural language processing, and robotic process automation) across all dimensions. This indicates the importance of AI applications in improving the quality of external auditing. Furthermore, the use of AI technologies has a positive impact on investor decisions through the quality of external auditing. Based on these findings, the researcher recommends that all accountants and auditors keep pace with technological developments and respond to changes in the surrounding environment by developing their AI skills. Audit firms should also be encouraged to adopt AI technologies in their work by replacing manual systems with computerized systems. The study aimed to analyze the impact of digital transformation indicators (ATMs, electronic payment cards, electronic point-of-sale systems, and instant settlement transfers). Adopting digital technologies has contributed to reducing reliance on cash and increasing the efficiency of monetary policy. However, in Iraq, limited effects were observed due to structural challenges such as weak infrastructure and economic instability.

Abstract

This research aims to test the possibility of designing, providing and monitoring the training service in accordance with the requirements of clauses (8, 10, 13) of ISO 29993:2017 in order to know the size of the gap between the requirements of these clauses of the standard and the actual reality in the training and development center of the Ministry of Construction, Housing and Public Municipalities. Which was chosen as a research sample. The research adopts a case study approach using the Check List as a data collection tool. Statistical methods were used to obtain the research results, represented by (the arithmetic mean and percentage to determine non-conformity and the actual size of the gap). The check list included Three axes, which are as follows (designing the training service, providing the training service, monitoring and evaluating the training service), as they are considered the main stages of the training process. The research reached a set of conclusions, the most important of which is that there is a gap between the actual reality of the researched center and the requirements of clauses (8, 10, 13) of the specification, as the total gap reached (34%) and is due to several long-established reasons that were diagnosed, while the percentage of conformity reached (66%). This is due to the center’s interest in the requirements of providing the training service.

The research recommended the necessity of applying the requirements of learning service providers outside formal education in accordance with ISO 29993:2017 to ensure the provision of high-quality training services and giving importance to the subject of documentation by directing departments to document all the operations they carry out in accordance with procedures, work instructions, or records to prove application.

Abstract

The research aims to identify the scientific and practical requirements that contribute to the success of the faculty members' tasks in the use of electronic accounting. To reach the objectives of the research, 47 questionnaires were distributed to accounting professors in Iraqi universities electronically to measure the extent of their use of electronic accounting in teaching the various accounting subjects and their compatibility with the quality standards of education, and distributed 157 questionnaires to the graduates of accounting departments in Iraqi universities electronically to measure their use of information technology And the extent to which they received electronic accounting education during their studies. The two researchers recommended the need to develop e-learning skills for teachers to achieve the quality of accounting education and give great attention to the subject of electronic accounting and inclusion in the courses of different stages by the accounting departments in Iraqi universities because of their effective role in the development of skills graduates and qualify them correctly in the labor market.

Abstract

Employee development is a fundamental pillar for the success of any organization striving for sustainable growth and excellence in performance. It encompasses a range of processes and activities aimed at enhancing the skills, knowledge, and capabilities of employees, leading to increased productivity and improved work quality. Through continuous training, self-learning, mentoring, and providing opportunities for career growth, employee development fosters loyalty and empowers individuals to contribute effectively to organizational goals.

Abstract

The study aims to know the relationship between the independent variable represented by marketing capabilities and their dimensions (market sensing capabilities, marketing learning capabilities, market targeting capabilities) with the dependent variable, which is agile marketing and its sub-dimensions (reducing excessive marketing operations, reducing surplus inventory for marketing operations, reducing inappropriate transportation). It is necessary to reduce excessive processing steps, reduce defective outputs in marketing, and reduce waiting times. The sample of the study was (50) male and female employees at various levels and administrative responsibilities specialized in providing oil products at the Vegetable Oil Factory, which is one of the important companies in Iraq that seeks to provide its products to customers in a way that exceeds competing companies. The research problem is represented by the limited interest in the Vegetable Oil Factory in Baghdad for marketing. Agile in his marketing work through the optimal use of marketing capabilities. And its lack of readiness for change in order to adopt the modern marketing philosophy to enhance its goals and marketing performance in the Iraqi business environment, in addition to the presence of many wasted money, effort, time and resources during the company’s marketing operations. To achieve the objectives of the study, a model was formulated for the study consisting of two main variables, and two main hypotheses were derived, the first related to correlation and the other to influence. The most important conclusion reached is that there is an acceptable presence of marketing capabilities in the vegetable oil factory that helps in achieving lean marketing.

Abstract

The research aimed to explore the reality of organizational intelligence and its dimensions — namely (environmental understanding, collective intelligence, and strategic vision) — as well as sustainable development and its dimensions (economic, environmental, and social). The focus was on Al-Ittihad Food Industries Company. The research also sought to identify the correlation between organizational intelligence and sustainable marketing within the company.

A descriptive approach was adopted, and the study sample consisted of 99 employees from various departments of the company. A questionnaire was designed as the main tool for data collection, in addition to conducting some personal interviews. Data were analyzed using SPSS software and Cronbach's Alpha for reliability testing. The questionnaire relied on a five-point Likert scale.

The research arrived at several key findings, most notably:

The level of organizational intelligence and sustainable marketing practices in the company was moderate, There is a strong positive correlation between the dimensions of organizational intelligence and those of sustainable marketing, The results indicated that the t-test values were statistically significant for all research variables at a significance level of (α ≤ 0.05), confirming a meaningful and positive correlation between organizational intelligence and sustainable marketing.

The study recommended that Al-Ittihad Food Industries Company should place greater emphasis on continuous learning and the need to clarify visions, plans, and future directions with its employees.

Abstract

Artificial intelligence (AI) applications are essential for enhancing payment security and improving customer experience in financial institutions. This research aims to examine the challenges faced by Mastercard in Australia and Visa in Singapore in implementing these technologies. The study focuses on three main areas: challenges related to fraud and privacy, the strategies employed by both companies to address these challenges, and the impact of AI applications on customer satisfaction and trust. The research adopts an analytical methodology that combines both qualitative and quantitative data, relying on diverse sources including academic studies and annual reports. The findings reveal that the effectiveness of AI applications varies across the two markets, reflecting different responses to local challenges. For instance, Mastercard demonstrates a greater reliance on machine learning technologies in Australia to combat fraud, while Visa in Singapore focuses more on enhancing privacy and data protection. The study offers strategic recommendations aimed at improving payment security and customer experience, such as increasing transparency in data usage and strengthening communication with customers. This research contributes to a deeper understanding of the role of AI in financial services, providing valuable insights for companies and practitioners in the sector. By addressing the unique challenges of each market, customer satisfaction can be enhanced and greater trust in digital payment services can be fostered.

Abstract

This study investigates the impact of Internet of Things (IoT) technologies on enhancing academic interaction between students and faculty members in higher education institutions, with an in-depth comparative analysis of the Iraqi context in relation to the Arab and global contexts. The study adopts a descriptive-analytical approach based on a literature review and comparative analysis, supported by statistical charts and a field survey. The findings revealed that IoT-enabled smart classrooms improved interaction indicators by between 10% and 96%. The global market reached USD 14.24 billion in 2024 and is projected to reach USD 48.26 billion by 2033, representing a compound annual growth rate of 15.3%. In contrast, the adoption rate of IoT in Iraqi public universities does not exceed 18%, compared with 68% globally, revealing a structural gap that requires urgent strategic intervention.

Abstract

The study aims to analyze the role of strategic digital entrepreneurship in addressing unemployment through indicators of strategic human resource resilience in digital marketing and e-services companies in Najaf Governorate. This comes in light of the rapid digital transformation that has reshaped business environments and opened new horizons for job creation, necessitating the adoption of strategic entrepreneurial practices that enhance competitiveness and contribute to the development of human resources. The study adopted a descriptive-analytical approach, and data were collected through questionnaire distributed to a sample of (43) individuals out of a total population of (80). The study focused on the dimensions of strategic digital entrepreneurship (opportunity recognition, resource orchestration, innovation, and market positioning), and their impact on addressing unemployment through (job creation, skills development, education and training, social innovation, and support for vulnerable groups), while examining the role of strategic human resource resilience (strategic learning, strategic memory, and strategic DNA). The results revealed a significant effect of strategic digital entrepreneurship on addressing unemployment, as well as the important role of strategic human resource resilience in enhancing organizations’ ability to adapt to digital transformation and leverage it to create job opportunities and develop skills.

Abstract

This study examines the role of university 0.4—a conceptual framework integrating technology, accessibility, and inclusive policies—in upholding the right to education at Al-Mustaqbal Private University, the research assesses how digital transformation, adaptive learning systems, and institutional policies contribute to equitable access, quality education, and student success. Data was collected via surveys (N=73 faculty), interviews with administrators, and analysis of academic performance metrics. Findings suggest that University 0.4 initiatives, such as virtual classrooms, AI-driven support tools, and financial aid programs, significantly reduce barriers for marginalized groups, including low-income and disabled students. However, challenges like digital literacy gaps and infrastructure limitations persist. The study concludes with recommendations for scaling University 0.4 innovations to align with SDG 4 (Quality Education) and national educational rights frameworks.

Abstract

This study aimed to identify the extent of the availability of soft skills in the performance of faculty members in Saudi universities from their point of view by applying it to faculty members at Imam Muhammad bin Saud Islamic University and Shaqra University, and to rank the soft skills according to the degree of their importance to them and whether there are statistically significant differences when The level of significance (a=0.05) in the degree of availability of soft skills in their performance is due to the variable of years of teaching or college experience.

The study followed the descriptive approach to achieve its objectives, as the researcher used the questionnaire as the main tool for the study, which contained (43) items distributed over six axes: critical thinking skill, problem solving, time management skill, leadership skill, decision-making skill, teamwork skill, and communication skill

The study concluded that soft skills were highly available in the performance of faculty members at Imam and Share Universities, as the highest arithmetic average for a sub-skill reached (4.85), with a rate of (97%), with a very high degree of availability, while the average for the lowest sub-skill reached (3.61), with a rate of (71%). With a high degree of availability.

 The skill of communication and communication came in first place in terms of its importance with an arithmetic average of (4.42), with a percentage of (88.2%) and a very high degree of availability. It was followed by the teamwork skill in second place with an arithmetic average of (4.28), with a percentage of (86%) and a degree of availability. Very high, while the time management skill came in third place with an arithmetic average of (4.18), with a percentage of (83.5%) and a high degree of availability, and in fourth place came the leadership skill with an arithmetic average of (4.12), with a percentage of (80.1%) and a high degree of availability. The skill of critical thinking and problem solving came in fifth place with an arithmetical average of (4.04), with a percentage of (80.1%) and a high degree of availability, while the decision-making skill came in last place with an arithmetical average of (4.03), with a percentage of (80%) and a high degree of availability.

Abstract

Universities worldwide have recognized the challenges of digital transformation in the face of rapid technological advancements, which have become an integral part of the educational system for all academic programs. This integration brings numerous advantages to all stakeholders in the educational process. Traditional methods, techniques, and tools have become outdated, prompting universities to enter into agreements with specialized global companies in technology, software, and tools. These agreements aim to equip their infrastructure for the educational process across various academic programs and disciplines, building knowledge and skills for students and preparing them for the job market in the era of digital transformation affecting all sectors.

Digital transformation is no longer an option but a necessity for institutions striving to enhance their efficiency and ensure their sustainability. The rapid growth of digital technology, advancements in smart devices and systems, increased data processing capabilities, and artificial intelligence have led to revolutionary changes. Dealing with the digital society requires investment in human resources and competencies possessing digital skills, capabilities, passion, ambition, and dialogue. This entails adopting a comprehensive approach to teaching methods, focusing on future requirements.

As mentioned earlier, digital transformation has become an urgent necessity that educational institutions seek to establish and dedicate. Educational technologies and digital platforms are not just concepts; they are now essential practices for the educational and learning processes. Universities need to leverage digital technologies to improve educational processes and practices, enhance learner and teacher experiences, and support the creation of effective and enduring educational models. Digital transformation helps universities operate more efficiently, remain competitive in global rankings and accreditations, and succeed in preparing learners for digital work environments.

In this context, digitization is a necessity in higher education institutions, attracting top students and improving the experience of courses, educational materials, and training processes in general. It enables monitoring and tracking to identify obstacles and challenges in training and reduce the risk of dropout. However, hesitation in understanding and seizing opportunities to move towards this digital environment still exists. It is essential for digital transformation to align with communication principles, ensuring its commitment to meet the expectations of various groups interested in economic, social, and environmental dimensions.

Abstract

The study aims to determine the level of techniques used in the State Company for Electrical Industries to optimize the supply chain that seeks to meet the needs and requirements of customers. In order for the company to survive and grow, especially in light of the increasing competition, it must develop or update its production system in line with the business environment, This study came to address the problem faced by the State Company for Electrical Industries, . The study relied on the descriptive and analytical approach in surveying the opinions of the study sample members in the General Company for Electrical Industries in collecting data and information needed for modern technologies and streamlining the supply chain in the practical aspect. Various specializations (administrative, engineering and technical) by (84%), the results were analyzed with the adoption of the statistical program SSPS V.25). And that the company’s management gives attention to streamlining the supply chain and is interested in maintaining the level of performance of its production operations by following the necessary procedures and instructions in order to reduce the negative effects on production processes to the maximum extent possible