Automatic detection of students’ engagement during online learning: A bagging ensemble deep learning approach

 The COVID-19 pandemic has reshaped education and shifted learning from in-person to online. While this shift offers advantages such as liberating the learning process from time and space constraints and enabling education to occur anywhere and anytime, a challenge lies in detecting student engagement during online learning due to limited interactio...

students engagement online learning deep learning Student online learning system online class training

Implement Software Solutions to Reduce Student Dropout Rates at Various Educational Stages

 Background: Student dropout rates in India are influenced by socio-economic and educational factors, affecting marginalized communities the most. Addressing dropout rates is essential for equitable education and socio-economic development. The National Education Policy (NEP) 2020 emphasizes the importance of reducing dropout rates and ensuring qual...

Reduce Student Dropout Rates Predicting Student Dropout Using Machine Learning smart india hackathon Student Dropout Reduction System machine learning

Smart Competency Diagnostic and Candidate Profile Score Calculator

 Project Concept: Comprehensive Employment Platform/Portal The current employment portal lacks a personalized and adaptive approach to job matching and skill development. There is a need for an intelligent system that not only matches job seekers with potential employers but also identifies and suggests training courses to bridge skill gaps. We wi...

Smart Competency Diagnostic Competency Score Calculations smart india hackathon Artificial intelligent machine learning

A Human-Machine Agent Based on Active Reinforcement Learning for Target Classification in Wargame

 To meet the requirements of high accuracy and low cost of target classification in modern warfare, and lay the foundation for target threat assessment, the article proposes a human-machine agent for target classification based on active reinforcement learning (TCARL_H-M), inferring when to introduce human experience guidance for model and how to au...

Active reinforcement learning adaptive learning wargaming environments machine learning algorithms and human input cutting-edge technologies

Distributed Group Coordination of Multiagent Systems in Cloud Computing Systems Using a Model-Free Adaptive Predictive Control Strategy

 This article studies the group coordinated control problem for distributed nonlinear multiagent systems (MASs) with unknown dynamics. Cloud computing systems are employed to divide agents into groups and establish networked distributed multigroup-agent systems (ND-MGASs). To achieve the coordination of all agents and actively compensate for communi...

Beneficial Perturbation Network for Designing General Adaptive Artificial Intelligence Systems

  The human brain is the gold standard of adaptive learning. It not only can learn and benefit from experience, but also can adapt to new situations. In contrast, deep neural networks only learn one sophisticated but fixed mapping from inputs to outputs. This limits their applicability to more dynamic situations, where the input to output mapping...

Machine Learning for Automating the Design of Millimeter-Wave Baluns

 We propose a framework to analyze mm-wave baluns directly from physical parameters by adding a dimension of Machine Learning (ML) to existing electromagnetic (EM) methods.From a generalized physical model of mm-wave baluns, we train physical-electrical Machine Learning models that both accurately and quickly compute the electrical parameters of mm-...