Abstract:
The Integration of Artificial Intelligence (AI) in university administration presents transformative opportunities for enhancing collaborative leadership. This paper explores how AI technologies—such as machine learning, natural language processing, and predictive analytics—can support decision-making, streamline administrative processes, and foster a culture of collaboration among university leaders. AI-driven tools can analyze vast amounts of data to provide insights, identify patterns, and predict trends, which can inform strategic planning and policy development. Through case studies and analysis, we examine the current applications and potential future developments of AI in higher education administration, highlighting successful implementations and lessons learned. Key considerations, including data privacy, ethical use, change management, and system integration, are discussed to provide a comprehensive understanding of the challenges and benefits associated with AI implementation. The role of AI in automating routine tasks, such as scheduling, resource allocation, and communication, is also explored, demonstrating how it can free up time for administrators to focus on more strategic and creative endeavors. Ultimately, this paper proposes new paradigms for collaborative leadership, emphasizing how AI can help universities navigate the complexities of modern administration and achieve greater efficiency, effectiveness, and innovation, while maintaining a commitment to ethical standards and stakeholder engagement.
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Nashwa Elabied. Collaborative Leadership With AI: New Paradigms in University Administration. A Comparative Analysis. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(3), pp. 01-06, 2024.
https://doi.org/10.55083/irjeas.2024.v12i03001
Abstract:
As more people use credit cards, fraudulent usage has become more widespread to keep up with the demand. The disadvantage is that it depends on developing an accurate CCFDS. In order to increase the security of financial transaction systems in an independent and effective manner, financial institutions need to construct a CCFDS that is accurate and efficient. During the process of detecting fraudulent activity involving CC, even if seemingly little aspects of the fraud are neglected, there is a dangerous possibility that these errors may turn out to be significant obstacles down the road. The detection process is a very tricky task since its dataset consists of substantial class imbalances. It biases classifier models towards the validation set, resulting in high train and validation accuracies but inordinately large false positives or false negatives. Financial institutions such as banks, etc. use credit card fraud detection are algorithms for matching transactional statistics and guesstimating whether the upcoming transaction is a fraudulent one or not. Moreover, institutions’ resources might be directed toward more questionable transactions in order to reduce fraud levels. This paper is used to GB based ML technique for CCFDS and achieve good accuracy.
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Shivam Singh et.al. Credit Card Fraud Detection using Gradient Boosting Machine Learning Techniques. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(3), pp. 07-15, 2024.
https://doi.org/10.55083/irjeas.2024.v12i03002
Abstract:
In the rapidly evolving landscape of healthcare management, the Satellite Medical Model has emerged as a strategic solution to enhance service delivery by decentralizing care through a network of satellite facilities connected to a central hub hospital. This model not only improves accessibility and optimizes resource utilization but also addresses workforce shortages, particularly in underserved regions. Rooted in the hub-and-spoke design from the transportation sector, the Satellite Medical Model facilitates efficient healthcare services, allowing primary care units to manage routine cases while more complex conditions are referred to specialized facilities. Despite its advantages, the implementation of this model faces significant challenges, including technical issues related to data security and interoperability, financial constraints, and operational complexities. This paper explores the potential of the Satellite Medical Model in modern hospital management, examining both its benefits and challenges. By identifying strategic approaches to mitigate these barriers, this study aims to provide healthcare leaders with actionable insights to effectively adopt this model, ultimately leading to improved patient outcomes and greater operational efficiency in healthcare delivery.
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Dr. Khaled El. A Review- Optimizing Healthcare Delivery: Navigating the Challenges and Benefits of the Satellite Medical Model in Modern Hospital Management. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(3), pp. 16-21, 2024.
https://doi.org/10.55083/int/irjeas.2024.v12i03001
Abstract:
The manipulation of facial features in videos and images, known as deepfake technology, poses a significant threat to security and digital forensics. This advanced technology can generate extremely realistic yet completely fabricated visual content, posing challenges in verifying authenticity. While current deepfake detection approaches are effective in controlled settings, they struggle to keep up in real-world scenarios due to the growing complexity and diversity of deepfakes. These techniques typically utilize convolutional neural networks (CNNs) to detect inconsistencies and artifacts in altered images and videos, but they have limitations such as overfitting, hefty computational demands, and struggles in adapting to new, unseen deepfake methods.
In this paper, an improved convolutional neural network (CNN) structure is suggested to enhance the identification precision and resilience against different types of deepfake alterations. The method involves using advanced preprocessing methods, such as more advanced face detection and extensive data augmentation, to increase the variety and robustness of the training dataset. By integrating extra convolutional layers and residual connections into the CNN structure, our model becomes more capable of capturing complex features and patterns related to deepfake alterations.
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Deepak Puri Goswami, Pankaj Pandey. Advancements in Deepfake Detection: A Novel Approach Using Enhanced Convolutional Neural Networks. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(3), pp. 22-34, 2024.
https://doi.org/10.55083/irjeas.2024.v12i03004