This research was conducted using a survey research method to investigate the influence of Artificial Intelligence (AI) on Nigerian students’ academic performances in tertiary institutions. Nigerian tertiary institutions have an estimated population of about 2.5 million students across the universities, polytechnics, monotechnics, and colleges of education. A sample size of 509 was used. The researchers adopted an online questionnaire (Google Form) to administer questions to respondents across Nigeria to elicit responses from the respondents bordering on their awareness and the use of AI and its attendant impacts on their academic performance. Five research objectives were raised for the proper investigation of this study. From the findings of the study, the researchers found that the majority of Nigerian students use AI and that AI has positive impacts on the educational performance of Nigerian students. It was also found that Nigerian students have training on the use of AI for educational purposes and that they are more familiar with Snapchat AI and ChatGPT. Conclusively, AI is useful to students in the sense that it enhances their knowledge of their courses, improves their learning and speaking skills, and helps them to have a quick understanding of their course by way of simplifying technical aspects of their courses. The researchers therefore recommend as follows: Nigerian tertiary institutions should formally train students as well as teachers on the use of AI for academic purposes so that they can understand the ethical implications of the use of AI. Using AI for writing could be interpreted to mean examination malpractice, and this should not be condoned in the educational sector; however, at the moment, a small number of students used AI for examinations. Albeit, the appropriate use of AI should be fully integrated into Nigerian tertiary institutions’ curricula.

The growth of mobile Internet has facilitated access to information by minimizing geographical barriers. For this reason, this paper forecasts the number of users, incomes, and traffic for operators with the most significant penetration in the mobile internet market in Colombia to analyze their market growth. For the forecast, the convolutional neural network (CNN) technique is used, combined with the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit (GRU) techniques. The CNN training data corresponds to the last twelve years. The results currently show a high concentration in the market since a company has a large part of the market; however, the forecasts show a decrease in its users and revenues and the growth of part of the competition. It is also concluded that the technique with the most precision in the forecasts is CNN-GRU.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

This research aimed to investigate the role of humanizing leadership in enhancing the effectiveness of change management strategies within organizations. Specifically, it focused on how humanizing leadership influences change outcomes and the extent to which organizational culture moderates this relationship. The study addressed critical questions regarding the impact of leadership behaviors, such as model vulnerability, emotional intelligence, open communication, and psychological safety on effective change management and employee performance. A quantitative approach was employed to provide a comprehensive analysis of the phenomena. Quantitative data were collected from a sample of 325 employees through surveys that measured perceptions of Humanizing leadership behaviors, organizational culture, and change outcomes. Data was analyzed by IBM SPSS 26.0. The findings revealed that humanizing leadership behaviors significantly enhances the success of change initiatives, primarily through improved employee engagement and reduced resistance. Organizational culture was found to play a moderating role, amplifying the positive effects of empathetic and inclusive leadership practices. The study provides actionable recommendations for organizational leaders and managers to foster a culture that supports humanizing leadership. By adopting leadership strategies that emphasize vulnerability, empathy, and inclusivity, organizations can enhance their adaptability and resilience against the backdrop of continuous change. These findings are particularly valuable for enhancing managerial practices and informing policy within corporate settings.