Journal of Engineering and Technology Management 73 (2024), 2024
Face detection has become a pivotal technology in various applications, such as security systems,... more Face detection has become a pivotal technology in various applications, such as security systems, user authentication, and social media platforms. The integration of machine learning (ML) techniques has significantly improved the accuracy and efficiency of face detection systems. However, deploying and optimizing these systems for large-scale applications remain challenging, particularly when considering computational resources, latency, and scalability. Cloud computing offers a promising solution by providing scalable infrastructure and diverse machine learning services that can handle large volumes of data and complex algorithms efficiently. This paper explores the optimization of face detection performance using cloud-based machine learning services. It examines various cloud platforms, such as AWS, Google Cloud, and Microsoft Azure, focusing on their machine learning tools and services like AWS Rekognition, Google Cloud Vision, and Azure Face API. The study also evaluates the performance of different face detection models deployed on these platforms, considering factors such as processing speed, accuracy, and cost-effectiveness. Through empirical analysis and experiments, this paper identifies best practices and strategies for optimizing face detection systems in cloud environments, offering insights for both researchers and practitioners in the field. The results demonstrate that cloud-based machine learning services can effectively enhance face detection performance, making them suitable for real-time applications in various domains.
International Journal of Scientific Research in Multidisciplinary Studies, 2024
The rapid evolution of web technologies demands innovative educational methods that balance theor... more The rapid evolution of web technologies demands innovative educational methods that balance theoretical instruction with practical experience. This paper examines the integration of GrapesJS, an open source web builder, into educational platforms hosted on AWS (Amazon Web Services) to enhance web development training. By leveraging AWS's cloud infrastructure, educational institutions can provide scalable, secure, and interactive learning environments. The study presents case studies, performance evaluations, and student feedback to assess the impact of this approach on web development education. Results indicate significant improvements in student engagement and learning outcomes, demonstrating the potential of combining GrapesJS and AWS for modern web development training.
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Papers by mahesh bagwani