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.
