In this study, we tackle the understudied area of Artificial Intelligence (AI) and its role in examining how modern revolutions may affect political systems across the Middle Eastern region. despite hundreds of studies documenting Middle Eastern uprisings over the past three decades, there has been little effort to harness AI to better understand or predict these multifaceted events. This study seeks to address this gap by assessing the performance of AI-intelligence in analyzing (broadly) revolutionary processes and their effects on regional political systems. The research uses a mixed-method methodology that involves a systematic literature review of contemporary scholarly articles, and an analytics study using AI tools. Our results show that AI-driven sentiment analysis can accurately track shifts in public opinion over the course of an entire revolution with a 40
Smart meters are critical for accurate energy monitoring and management, yet they can malfunction, leading to missing values in energy datasets. This study evaluates the performance of various statistical and Machine learning methods for imputing missing values in energy data, specifically focusing on production from photovoltaic (PV) systems and electricity consumption across different building types. This study focus on the Missing Completely at Random (MCAR) mechanism and two distinct scenarios are evaluated: (A) missing values occurring between two existing values, and (B) blocks of 3–5 consecutive missing values. The effectiveness of the methods is assessed as the fraction of missing values increases (5
Gender classification is crucial in fields like security, biometrics, and sports science. Traditional methods relying on physical characteristics, such as genital shape, have become less reliable due to modern challenges like sex reassignment surgeries. This study proposes a more reliable approach using anthropometric data from the ANSUR II dataset, applying machine learning algorithms such as Logistic Regression, Neural Networks, and SVMs. Our results show that high classification accuracy (up to 100
This study aimed to investigate the role of artificial intelligence (AI) applications in developing the academic and teaching skills of university professors, focusing on how AI integration influences teaching practices across different academic fields and levels of experience. A quantitative approach was employed using a structured questionnaire, administered to 150 university professors from various disciplines. The survey examined the extent of AI integration, perceived impacts on teaching and academic skills, and challenges encountered in adapting AI tools. Findings indicate moderate AI adoption, with higher integration among engineering and technology professors, and lower usage in social sciences and humanities. Professors with less teaching experience showed greater adaptability to AI tools. Key challenges identified include limited training, tool complexity, and data privacy concerns, yet many professors noted increased teaching efficiency and enhanced research capabilities due to AI. The study highlights AI’s potential to improve teaching practices and academic performance. However, it emphasizes the need for comprehensive training, equitable access to resources, and clear ethical guidelines to maximize AI’s effectiveness in academia. The findings provide insights for institutions aiming to support professors in adopting AI for academic and professional development.
A correspondent bank serves as a financial institution that offers its services to foreign financial institutions by eliminating the need for physical branches abroad. Efficiently managing the funds allocated to correspondent banks is important for profitability and customer satisfaction. In this study, we focus on predicting daily demand for a domestic bank’s correspondent bank branches in Turkey. For this purpose, we apply statistical and deep learning-based time series forecasting techniques using transaction count data. The experiments are performed on data involving transactions between 2015 and 2021 belonging to 20 correspondent banks. The obtained results indicate that hybrid CNN-LSTM-based model gives promising results for accurate correspondent bank transaction forecasting. It is also observed that the model performance is highly influenced by the specific characteristics of the transaction count time series data. The proposed system offers practical applications, including optimizing fund allocation, risk management, improving customer service through timely transaction processing, and foreign exchange management for both domestic and correspondent banks.
This work is a case study in understanding the argumentative functions of metaphor within literary discourse by means of novel integration of AI techniques with traditional literary analysis, specifically applied to the poetry of Ibn al-Jayyab al-Gharnati (d. 815H), one of the most celebrated classical Arabic poets. The nature of metaphors in Arabic poetry has often been historically valued for aesthetic or symbolic reasons, with limited research investigating their argumentative and persuasive use. Using artificial intelligence and natural language processing (NLP) algorithms, this study implements a systematic approach to extract metaphorical expressions from the poetry of Ibn al-Jayyab and analyses their contribution in creating argumentative accountability as well as rhetorical effectiveness. It employs a mixed-methods design that reveals not only the frequency, but also the functional distribution of metaphors in Ibn al-Jayyab’s poetic corpus. This first step qualitative analysis looks at the context and purpose of single metaphors to reveal its argumentative goals for the poet. While Mazumdar’s work is primarily qualitative and relies on close readings of texts to interpret metaphors, the quantitative analysis uses statistical methods to find trends in metaphor use—how some metaphoric frames (such as light versus darkness or journey versus path) become repeated motifs that emphasize important philosophical and ethical points. The results show that Ibn al-Jayyab’s metaphors are not ornamental, but rather intentional rhetorical devices that reinforce the poet’s claims and push readers towards certain interpretations. For instance, light and darkness symbolize knowledge and ignorance respectively, which creates an orientation of the audience ‘identities and values. Additionally, the study shows how AI tools help design in revealing metaphorical tabular structures to readers that might otherwise be obscured to human analysis alone, confirming the valued place of AI as augmenting literary studies.
The present study investigates the pragmatics of argumentation in Radwa Ashour’s Granada trilogy, employing Artificial Intelligence (AI) to reveal and explore the subtle argumentative framework behind text. The problem with traditional literary analysis is that it does not go well with the complexity and subtlety of argumentative discourse in rich historical texts. Using AI tools like Natural Language Processing (NLP) and machine learning, this study presents a new way to analyze the combination of characters, dialogues and narratives that come together to transmit an ideological message. The findings reveal that Ashour’s characters are intricately engineered to reflect the socio-political context of the time and define direct and indirect dialogue as the center of resistance, culture, and survival. The present study thus both advances scholarly knowledge concerning spatial imaginations of nations and finds a broader utility where AI augments approach to text that are resonant with the social sciences. While literary analysis mediated through AI in this way promises exciting new interdisciplinary work, potential for probing the complexity of narrative argumentation. The paper ends with some suggestions on how to integrate AI more into literary research, as well as a comment on the necessity to create better suited AI tools for Humanities.
This study aims to accurately forecast depth maps and estimate volume from 2D images. Medical imaging, robotics, and computer vision all have the issue of prediction using 2D data. Traditional methods have struggled to generalize across many scenarios and datasets. To overcome these limitations, a novel deep learning-feature extraction method has been devised. A U-Net model predicted depth maps more accurately by capturing complicated spatial hierarchies with its powerful convolutional network architecture. HOG (Histogram of Oriented Gradients) and Oriented FAST, Rotated BRIEF (ORB) feature extraction enhanced the model’s object volume estimation. A custom Blender-generated dataset and the NYU Depth V2 dataset were used to validate the recommended algorithms. In testing using Random Forest, Support Vector Regression (SVR), and Gradient Boosting Machine (GBM), the recommended technique outperformed them. Improvements in the R-squared (R2) and Mean Squared Error (MSE) metrics are observed. The results demonstrate the effectiveness of deep learning with traditional feature extraction and regression models, paving the way for more accurate volume estimation from 2D images. This study discusses a strong framework that enhances depth and volume estimate accuracy, and the framework is scalable and domain-adaptable. The findings indicate that the proposed approach may be used for other applications that need precise 3D reconstructions from 2D data, which is significant for future study.
Analyzing the Concept of AI-Supported Journalism as a Factor of Promoting the Culture of Dialogue between Civilizations. This paper examines the potential for artificial intelligence technologies to revolutionize journalistic content by being more inclusive and culturally aware, as well as improving audience reach and engagement. The current study employing a descriptive research design seeks to explore the potential integration of AI tools in media practices for civilizational dialogue through a structured questionnaire targeting media professionals. According to the research, AI-driven journalism can promote peaceful cohabitation, mutual respect, and thus act as a bridge between cultures. The article further emphasizes the role of media establishments and civil society to hold each other accountable and urges the need for specialized courses enabling journalists to be equipped with the use of AI in their work. These are intended to elevate the role of AI in enhancing cross-cultural understanding and supporting global peace.
This research examines the challenges and opportunities offered by AI in education, more specifically in terms of English Language Learning (ELL). Using qualitative research methods, a combination of document analysis and expert interviews, the paper uncovers some key themes in AI in language education. This research underlines the grey areas of AI in ELL, as it can contribute a lot in personalized learning but also embed some digital divide problems or must be balanced with conventional methods. Our analysis yielded four major themes: personalization, access and digital divide, over-dependence on technology, and educator growth. The most prevalent theme was personalization, highlighting the ability of AI to customize learning experiences for individual learners. Nonetheless, this advantage is offset by the issue of fair access to AI tools and the danger of excessive reliance on technology. The research looks at different AI tools such as Intelligent Tutoring Systems, Personalized Learning Platforms, and Chatbots and Natural Language Processing NDLR tools and discusses the advantages and disadvantages of using these popular forms of Academic Librarianship-Informed language learning TA in language learning contexts. The implications highlight the importance of a balanced strategy in AI deployment, one that leverages its benefits while minimizing potential harm. The paper ends with implications for policymakers and educators on how to use AI technology responsibly in language education. They include creating clear ethical frameworks, providing fair access to AI tools for all students, supporting continuous professional development of teachers, and long-term studies to investigate the lasting impact of AI on language learning. This analysis serves as inspiration for the future of language education with AI.
This study explores the role of AI-powered digital newspapers in bridging perspectives and enhancing civilizational dialogue, with a specific focus on Iraqi university professors specializing in media. Using a descriptive survey method, the study gathered data from a random sample of 40 professors from the University of Baghdad and the University of Anbar. The research examines how digital newspapers, supported by artificial intelligence tools such as personalized content, sentiment analysis, and real-time translation, contribute to fostering intercultural understanding and facilitating constructive dialogues between diverse cultural perspectives. The findings reveal that digital newspapers are highly preferred by the participants compared to other digital media platforms, mainly due to their speed of access, wide reach, and ease of use. Additionally, the study highlights the significant impact of digital newspapers in providing intellectual and cultural environments that promote acceptance of differing ideas and support dialogue among varying viewpoints. The results underscore the importance of AI in shaping media content that bridges cultural divides, thereby enhancing the role of digital newspapers in civilizational dialogue. The study recommends further research on the public’s perception of civilizational dialogue and the potential of digital media, particularly in the context of AI, to play a more substantial role in global intercultural communication.
Parkinson’s disease (PD) is a neurodegenerative condition that severely impairs motor function and reduces quality of life. Effective disease management depends on early and precise diagnosis. Recent advancements in artificial intelligence have significantly enhanced the accuracy of PD diagnosis, particularly through the analysis of speech data. In this paper, we propose a machine learning algorithm based on voice features to classify PD and Healthy Controls (HC). We employed various machine learning classifiers, including Random Forest (RF), Logistic Regression, KNN, Support Vector Machine (SVM), and XGBoost. After hyperparameter tuning, XGBoost achieved the highest accuracy at 96.67
Within the context of business process management (BPM) and process mining, anomalies, often defined as deviations from the standard flow of a given process, has the potential to significantly impact a businesses lifecycle. Anomalies which can represent anything from a simple inefficiency to fraudulent activity, is often times reflected within the event logs of the digitalized business processes. The detection of said deviances can help boost a businesses efficiency and protect it against fraudulent activity. Furthermore, by understanding the causes of these deviances businesses can further optimize their processes increasing their profitability. In this study we leveraged two different approaches for explainable artificial intelligence on graph structured business process data. Initially, we build and test a total of 8 different Graph AutoEncoder (GAE) models consisting of 4 unique encoders and 2 different decoders as the key components of the architecture. After that we test dimension reducing standard AutoEncoder (AE) models with a novel type of AE working towards increasing the dimensionality of the latent feature representations. Once all the testing is done we pick the champion model and use the labels acquired from it to train a predictive Graph Neural Network (GNN) model to get features importances by leveraging two different types of explainable artificial intelligence approaches meant to work on graph structured data: GNNExplainer and GraphLIME. Using the aforementioned methodology, we analyze and unravel the anomalies within the logs of an anonymous tenant of the Next4biz BPM platform.
This study uses AI tools to explore social media use data and adolescent mental health outcomes. Using a mixed-method analysis of sentiment analysis, natural language processing (NLP) and machine learning algorithms, we examined public social media data from adolescents aged 13–18 years on three major platforms: Instagram, TikTok, and Twitter. The study applied VADER and BERT for sentiment analysis, as well as Random Forest and Support Vector Machine (SVM) algorithms on predictive approaches. The analysis showed that 42
This study explores the combined impact of artificial intelligence (AI) and social media on shaping international policy strategies. By analyzing their individual and synergistic contributions, the research highlights how AI enhances decision-making through predictive analytics, sentiment analysis, and misinformation detection, while social media facilitates real-time communication, public engagement, and crisis management. The findings underscore the transformative potential of these technologies in modern policymaking but also reveal significant challenges, including ethical concerns, algorithmic biases, and regional disparities in access. The study contributes to the field by bridging gaps in existing literature and offering actionable recommendations for policymakers to harness these tools effectively and responsibly. Limitations are acknowledged, and suggestions for future research are provided, emphasizing the need for further exploration into the long-term implications of these technologies on global governance.
Breast cancer (BC) remains a predominant concern among women globally. Striving to prevent and identify BC in its initial phases, the development of computer aided-diagnosis (CAD) is imperative. These systems play a pivotal role in precisely controlling tumor growth and administering tailored treatments based on the tumor’s pathological stage. The foundational step in creating such a system involves a crucial pretreatment phase aimed at enhancing the image boundaries and structures quality. Subsequently, the segmentation step becomes essential, particularly in the context of Medio-Lateral-Oblique (MLO) view mammograms, where the images encompass the pectoral muscle (PM) situated in the upper corner. This paper introduces a novel approach for PM removal in MLO mammogram observations, leveraging region, and edge-based concepts. The suggested method has been rigorously evaluated using digital mammography from the Mini-MIAS database, through the DICE Coefficient, Structural Similarity (SSIM) and Jaccard Similarty Index (JSI) metrics, providing insights into the segmentation quality against the ground truth. The findings affirm the effectiveness of the suggested approach in comparison to several other methods within the identical field.
The problem of task scheduling in distributed environments such as Cloud Computing is seen as a major challenge for ensuring efficient resource management and improving overall system performance. Traditional approaches to task scheduling based on heuristics and meta-heuristics have proven useful in some scenarios, but they have limitations. Given that Cloud Computing is a complex and dynamic environment, the challenge is to create a scheduling strategy that has the ability to adaptively understand the nature of this environment, and machine learning (ML) techniques are emerging as promising tools for this. To focus on this direction, we provide a review of ML-based approaches in a very specific context: dynamic scheduling of independent tasks in Cloud Computing. Through a deep analysis of these approaches, we also propose a comparative study between these strategies in terms of optimized metrics. We also underline the growing interest in hybrid approaches that combine ML techniques with meta-heuristics.
The paper is devoted to the application of AI methods for the analysis of plant communities based on remote sensing images. The analysis is carried out to select territories that can be included in the regional monitoring database of valuable steppe communities in the Samara Region. The analysis apparatus is an artificial neural network in the form of a multilayer perceptron used for pixel-by-pixel classification of remote sensing image series for several vegetation seasons. The paper proposes a way to aggregate the classification results for different seasons, taking into account the high confidence of belonging to classes. The feasibility of using relief in classification is investigated. To verify the results obtained by AI, ground-based surveys of the selected territories were carried out. These surveys confirmed the class affiliation of plant communities of a significant part of the selected areas, which proved the efficiency of the proposed approach and the feasibility of using AI methods to optimize field research.
This study explores the pivotal role of digital media in promoting social peace and addressing societal challenges such as extremism. With the advent of Web 2.0 technologies, digital media has transformed societal interactions, offering opportunities for cultural dialogue, tolerance, and awareness while also posing risks of misuse. The study emphasizes the dual role of digital media—serving as a tool for fostering harmony or fueling division depending on its ethical use. The research identifies key factors influencing social peace, including displacement, inequality, marginalization, and the dissemination of extremist ideologies. Furthermore, it examines the impact of imported values and the media's role in shaping societal perceptions. Through a descriptive methodology and a survey distributed among Iraqi media professionals and educators, the study analyzes media’s role in promoting peace and mitigating extremism. Results indicate that political and religious topics dominate media consumption, highlighting their influence on public awareness and societal dynamics. The findings reveal that digital media, when employed ethically and strategically, can enhance social cohesion by advocating for values such as tolerance, respect, and coexistence. However, the influx of foreign cultures and unethical media practices has negatively impacted on social harmony, emphasizing the need for comprehensive media policies. The study recommends collaboration between media institutions and community organizations to develop content that fosters dialogue and reduces extremism. It also highlights the importance of citizen journalism and educational initiatives to promote a culture of tolerance. By addressing gaps in media ethics and professionalism, this research provides actionable insights for leveraging digital media to build a more peaceful and inclusive society.
The study aims to evaluate the potential of Diclofenac derivatives as new drugs. For this purpose, the quantum mechanics and artificial intelligence methods are mixed. Using semi empirical PM3 implemented in HyperChem software, we developed and analyzed the properties of several Diclofenac derivatives. First and foremost, the physical properties, such as total energy, dipole moment, ionization energy, and electron affinity were calculated. The results were then compared with those of the Diclofenac molecule. It was discovered that a derivative with the –CH3 group shows identical stability and polarity to the original drug. It means that the derivative has similar therapeutic properties, whereas the pharmacokinetic properties may vary. The –Br derivative shows the highest stability, meaning long-lasting character. However, the –F derivative, despite the high degree of stability, is not reactive enough. The –O derivative also requires modification for safety reasons. This work reveals the importance and significance of mixing quantum mechanics and artificial intelligence. It provides a model for estimating the efficacy and safety of a new drug, discovered through quantum mechanics. The work can be viewed in the context of the existing literature on the issue of computational drug design that advocates mixing the new technology with the old one.
