In the rapidly evolving realm of cloud computing security, this paper introduces an innovative solution to address persistent challenges. The proliferation of cloud technology has brought forth heightened concerns regarding data security, necessitating novel approaches to safeguarding sensitive information. The issue centers on the vulnerability of cloud-stored data, often necessitating enhanced encryption and key management strategies. Traditional methods often fall short in mitigating risks associated with compromised encryption keys and centralized key storage. To combat these challenges, our proposed solution encompasses a two-phase approach. In the first phase, dynamic Advanced Encryption Standard (AES) keys are generated, ensuring each file’s encryption with a unique and ever-changing key. This approach significantly enhances file-level security, curtailing an attacker’s ability to decrypt multiple files even if a key is compromised. The second phase introduces blockchain technology, where keys are securely stored with accompanying metadata, bolstering security and data integrity. Elliptic Curve Cryptography (ECC) public key encryption enhances security during transmission and storage, while also facilitating secure file sharing. In conclusion, this comprehensive approach enhances cloud security, providing robust encryption, decentralized key management, and protection against unauthorized access. Its scalability and adaptability make it a valuable asset in contemporary cloud security paradigms, assuring users of data security in the cloud.

This comprehensive study investigates the integration of cloud computing and deep learning technologies in medical data analysis, focusing on their combined effects on healthcare delivery and patient outcomes. Through a methodical examination of implementation instances at various healthcare facilities, we investigate how well these technologies manage a variety of medical data sources, such as wearable device data, medical imaging data, and electronic health records (EHRs). Our research demonstrates significant improvements in diagnostic accuracy (15–20% average increase) and operational efficiency (60% reduction in processing time) when utilizing cloud-based deep learning systems. We found that healthcare organizations implementing phased deployment approaches achieved 90% successful integration rates, while hybrid cloud architectures improved regulatory compliance by 50%. This study also revealed critical challenges, with 35% of implementations facing data integration issues and 5% experiencing security breaches. Through empirical analysis, we propose a structured implementation framework that addresses these challenges while maintaining high performance standards. Our findings indicate that federated learning techniques retain 95% model accuracy while enhancing privacy protection, and edge computing reduces latency by 40% in real-time processing. By offering quantitative proof of the advantages and difficulties of combining deep learning and cloud computing in medical data analysis, as well as useful recommendations for healthcare organizations seeking technological transformation, this study adds to the expanding body of knowledge on healthcare digitalization.

The integration of artificial intelligence into the realm of education has emerged as a consequence of using information and communication technologies as effective instruments to enhance the process of teaching and learning. In November 2022, an AI-driven chatbot known as ChatGPT was introduced. This chatbot possesses the capability to produce coherent and informative responses akin to those generated by humans in response to user inputs. ChatGPT demonstrates its adaptability as a potential facilitator of open education by providing assistance, guidance, and feedback to self-directed learners; thereby amplifying their motivation and involvement. The aim of this paper is to conduct a comprehensive analysis of the aforementioned aspects and delve into the prospective application of ChatGPT within educational frameworks. More precisely, its objectives encompass advocating integration of ChatGPT in educational contexts and furnishing educators with a repertoire of methodologies and approaches to guarantee the prudent and efficacious incorporation of ChatGPT in pedagogical or research endeavors. Through these efforts, the paper strives to foster a heightened and well-informed discourse about the utilization of ChatGPT within the scope of education. The paper outlines the essential strategies and methodologies that should be concurrently utilized when incorporating the services of ChatGPT in educational settings.

The recent advancements in Machine Learning (ML) and Deep Learning (DL) provide a new dimension in biomedical big data analysis, while the cloud computing technologies present the breakthroughs of handling massive data from hardware, software, and storage. Therefore, in this paper, an examination of the characteristics and challenges of big data is conducted with, highlighting the need for sophisticated analytical approaches. The paper then explores a wide spectrum of ML techniques, including unsupervised, supervised, and reinforcement learning methods, and their applications in big data contexts. This study explores recent advancement in DL architectures, while examining their effectiveness. Furthermore, we will show main challenges and limitations associated with using ML and DL in big data settings, including computational complexity, issues of data quality, model interpretability, and ethical concerns. Finally, it concludes by discussing the trends and future directions that are upcoming, including federated learning, explainable AI, and edge computing. Additionally, an experimental case study is presented to validate our approach, demonstrating its effectiveness in real-world healthcare scenarios through improved diagnostic accuracy and reduced computational time. Results have shown that the incorporation of DL techniques, such as CNN and RNN, forms the basis for large-scale analytics in medical data analytics due to their great contribution toward the development of accuracy and scalability. In specific research on disease prediction and medical image diagnosis within the healthcare industry, there was an improvement in diagnostics by up to 20%, with a reduction in computation time achieved via cloud-based architecture. These developments demonstrate the high extent of integration between DL and cloud computing in handling the challenge of bulk medical datasets and providing practical insight for both researchers and health professionals.

Artificial Intelligence (AI), a rapidly advancing field in the modern era, particularly within computer science, continues to progress by developing tools with superior capabilities and innovative features. This study aims to conduct a comparative analysis between ChatGPT-4 and its enhanced version, ChatGPT-4o, based on various parameters. The methodology employs a mixed-methods approach using a standardized dataset and systematically designed prompts. The findings reveal significant improvements, including enhanced coherence and contextual relevance of generated content, accurate and verifiable citation presentation, and improved capabilities in addressing scientific, educational, and technical inquiries. Additionally, ChatGPT-4o demonstrates superiority in handling complex creative writing tasks, text analysis, and understanding intricate scientific, literary, and logical scenarios. These results collectively indicate that the outputs of ChatGPT-4o are more effective, making it a reliable tool for academic, professional, and technical purposes. This research highlights the significant impact of ChatGPT-4o on user interactions, showcasing its capacity to fundamentally transform content creation processes and analytical functions across various fields. The implications of this study suggest that ChatGPT-4o has the potential to enhance productivity and innovation in education, professional industries, and technological development