CBDC-Net: Recurrent Bidirectional LSTM Neural Networks Based Cyberbullying Detection with Synonym-Level N-Gram and TSR-SCSOFeatures

Deep Learning is a cutting-edge technology which has a noteworthy impact in the real-world applications. The multi-layer neural nets involved in the blueprint of deep learning enables it to deliver a comprehensive decision-making system with quality of “think alike human cerebrum”. Deep Learning assumes an essential part in various fields like horticulture, medication, substantial business and so forth. Deep Learning can be well prompted in the remote sensing applications especially in perilous military applications. The location of land mines can be detected using a deep learning algorithm design technique aided with distinctive machine learning tools and techniques. The intelligent system designed by the deep learning process involves a massive dataset including the assorted features of the landmines like size, sort, dampness, ground profundity and so on. Incorporation of Geographical Information System can give a prevalent statistical analysis of the varied landmines. The multiple layers present in the deep learning neural schema may increase the feature extraction and the knowledge representation through increase in the complexities of landmines’ input sets. The likelihood of brokenness of landmines can be increased by the utilization of deep learning prediction model which enormously helps the survival of militaries, creating a social effect.

Deep Learning Algorithm Design for Discovery and Dysfunction of Landmines

S. Leelavathy,

S. Balakrishnan,

M. Manikandan

et al. 2024

AI-Driven Real-Time Feedback System for Enhanced Student Support: Leveraging Sentiment Analysis and Machine Learning Algorithms

J. Prakash,

R. Swathiramya,

G. Balambigai

et al. 2024

The rapid evolution of educational technologies has led to a shift toward personalized and adaptive learning experiences. A critical component of such systems is the ability to provide timely and relevant feedback to students. This paper presents an AI-driven real-time feedback system designed to enhance student support through the integration of sentiment analysis and machine learning algorithms. The system leverages sentiment analysis to gauge the emotional tone of student interactions, such as forum posts, assignment submissions, and feedback. Machine learning algorithms, including decision trees, support vector machines (SVM), and deep learning models, are used to analyze and predict student engagement, performance, and emotional states. By combining both cognitive and emotional insights, the system delivers personalized, context-sensitive feedback that helps students overcome learning challenges and improve academic outcomes. The effectiveness of the system is evaluated using multiple datasets, showing significant improvements in student engagement, satisfaction, and performance.

Developing an AI-Powered Interactive Virtual Tutor for Enhanced Learning Experiences

P. Rathika,

S. Yamunadevi,

P. Ponni

et al. 2024

The integration of artificial intelligence (AI) in education has opened new avenues for enhancing personalized learning experiences. This paper proposes the development of an AI-powered interactive virtual tutor designed to support students throughout their educational journey. The virtual tutor leverages advanced natural language processing (NLP) algorithms, sentiment analysis, and machine learning to engage students in real-time, providing tailored guidance, explanations, and feedback. By analyzing students' learning patterns, emotional states, and progress, the AI tutor offers personalized recommendations and interventions, enhancing both cognitive and emotional aspects of learning. The system’s interactive features, including voice recognition and conversational AI, allow students to interact naturally, facilitating a more engaging and immersive learning experience. This paper also presents the architecture of the proposed virtual tutor, key technologies involved, and its potential impact on student learning outcomes. Initial results demonstrate significant improvements in student engagement, satisfaction, and academic performance, suggesting that AI-driven virtual tutors could revolutionize personalized education..