International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 06 | Jun 2024
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p-ISSN: 2395-0072
Machine Learning In Education Rutuja Keshav Mankar, Shraddha Rajesh Gajbhiye, Nikita Bhagwat Patil, Prof T.P Raju Ms. Rutuja Keshav Mankar Student of MCA, Tulsiramji Gaikwad Patil Collage of Engineering and Technology Mohgaon(Nagpur), Mahrashtra, India. Ms. Shraddha Rajesh Gajbhiye Student of MCA, Tulsiramji Gaikwad Patil Collage of Engineering and Technology Mohgaon(Nagpur), Mahrashtra, India. Ms. Nikita Bhagwat Patil Student of MCA, Tulsiramji Gaikwad Patil Collage of Engineering and Technology Mohgaon(Nagpur), Mahrashtra, India. Prof. T.P Raju Department of MCA, Tulsiramji Gaikwad Patil Collage of Engineering and Technology Mohgaon(Nagpur), Mahrashtra, India. ------------------------------------------------------------***---------------------------------------------------------------
Abstract - Predictive maintenance has gained significant
Traditional recommendation systems, such as collaborative filtering and content-based filtering, have been widely adopted across various domains. However, these approachesoften suffer from limitations such as coldstart problems, sparsity of data, and lack of interpretability. In response to these challenges, recent advances in machine learning have spurred the development of more sophisticated recommendation algorithms that leverage techniques such as deep learning, reinforcement learning, and factorization machines. The impact of AI in education is recognized as transformative, offering learners a unique and wonderful educational experience. As AI operations in education continue to be explored and developed, the potential for enhancing learning outcomes becomes increasingly evident. The integration of AI technologies into educational processes holds promise for personalized and efficient learning pathways.
attention in various industries due to its potential to reduce downtime, minimize costs, and optimize maintenance schedules. In this paper, we present a comprehensive review of recent advances in machine learning algorithms for predictive maintenance applications. We discuss the challenges associated with traditional maintenance approaches and highlight the benefits of adopting predictive maintenance strategies. We then provide an overview of popular machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning, and discuss their suitability for predictive maintenance tasks. Furthermore, we review recent research efforts in feature engineering, model selection, and evaluation metrics tailored specifically for predictive maintenance applications. Finally, we present case studies and practical examples to illustrate the successful implementation of machine learning algorithms in real-world predictive maintenance scenarios. Our review aims to provide researchers and practitioners with insights into the current state-of-the-art in machine learning for predictive maintenance and to guide future research directions in this important domain.
In this paper, we aim to provide a comprehensive overview of the role of machine learning in personalized recommendation systems. We begin by discussing the fundamental concepts and challenges associated with recommendation systems, highlighting the need for advanced machine learning techniques to address these challenges effectively. Subsequently, we delve into the various machine learning algorithms commonly used in recommendation systems, outlining their strengths, weaknesses, and applications. Additionally, we explore recent research trends and emerging methodologies in the field, such as neural collaborative filtering, deep reinforcement learning, and multi-armed bandit algorithms.
Key words: Machine Learning in Education I. INTRODUCTION In recent years, the explosion of digital content and the proliferation of online platforms have led to an overwhelming amount of information available to users. As a result, personalized recommendation systems have become increasingly crucial for helping users navigate this vast sea of content and discover items that are most relevant to their interests. Machine learning techniques play a pivotal role in the development of these recommendation systems, enabling platforms to analyze user behaviour and preferences to deliver personalized recommendations in real-time.
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Furthermore, we present case studies and practical examplesto illustrate the implementation and performance of machine learning-based recommendation systems in real-world scenarios across different domains, including ecommerce, media streaming, and social networking platforms. By synthesizing insights from both academic research and industry practices, this paper aims to provide researchers, practitioners, and stakeholders with a
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