Inclusive learning aims to ensure equal access, participation, and success for all students, regardless of ability, background, or socio-economic status. Traditional standardized education models have often excluded diverse learners, but emerging educational technologies offer new opportunities to address these gaps. This paper analyzes how technologies such as AI-driven personalization, adaptive learning systems, virtual and augmented reality, learning analytics, assistive tools, and cloud-based platforms support inclusive education. These are examined within frameworks like Universal Design for Learning (UDL) and differentiated instruction, which emphasize accessibility, flexibility, and learner engagement. The study proposes a multi-layered framework for implementing inclusive educational technology, incorporating learner profiling, adaptive content delivery, accessible design, and continuous feedback through data analytics. Results indicate improvements in engagement, completion rates, accessibility compliance, and learner satisfaction. However, challenges such as data privacy, algorithm bias, infrastructure gaps, and the need for teacher training remain significant. The paper concludes that while technology can greatly enhance inclusive learning, effective implementation requires careful planning, supportive policies, and ongoing research.
Pooja Agarwal, Rakesh Chandra· International Journal of Eme...· 0 citations
Recent advances in machine learning have produced numerous predictive algorithms for classification, regression, and forecasting tasks. However, selecting the most suitable model for a specific dataset remains challenging, often requiring expert knowledge, extensive experimentation, and significant computational resources. To address this issue, automated model selection has emerged as an important research area within machine learning and intelligent decision-support systems. Meta-learning, or “learning to learn,” provides an effective solution by utilizing knowledge gained from previously analyzed datasets to predict the performance of learning algorithms on new datasets. It examines dataset characteristics, known as meta-features, and recommends appropriate machine learning models, thereby improving selection accuracy while reducing computational costs. This study proposes a comprehensive meta-learning framework for automated predictive model selection. The framework includes dataset characterization, meta-feature extraction, meta-dataset generation, algorithm evaluation, and meta-model construction. Statistical, information-theoretic, landmarking, and complexity-based features are used to describe datasets and train a meta-learning model capable of recommending suitable algorithms for new predictive tasks. The research evaluates several popular machine learning algorithms, including Decision Trees, Support Vector Machines, Random Forests, Naïve Bayes, Artificial Neural Networks, and k-Nearest Neighbor classifiers. Experimental results demonstrate that meta-learning significantly improves model recommendation accuracy compared to traditional trial-and-error approaches while reducing training time and computational overhead. As part of the broader field of Automated Machine Learning (AutoML), the proposed framework offers an intelligent algorithm recommendation system that supports efficient resource utilization and assists practitioners in selecting high-performing models without extensive machine learning expertise. The findings highlight the potential of meta-learning-based model selection for future intelligent analytics, decision-support, and large-scale data mining systems.
Pooja Agarwal, Rakesh Chandra· International Journal of Mac...· 0 citations
Rapid urbanization, industrialization, and aging infrastructure have increased the need for efficient monitoring systems. Traditional manual inspections of bridges, tunnels, pipelines, dams, railway tracks, and industrial facilities are costly, time-consuming, labor-intensive, and risky. Autonomous inspection robots offer an advanced solution for smart infrastructure monitoring and maintenance. This study reviews autonomous inspection robots developed before February 2019, focusing on their design, navigation, sensors, communication systems, and control methods. These robots use technologies such as LiDAR, ultrasonic sensors, infrared cameras, thermal imaging, GPS, and wireless communication for real-time monitoring, defect detection, and predictive maintenance. Machine learning and computer vision further improve inspection accuracy. Different robot types, including wheeled, tracked, aerial, climbing, underwater, and hybrid robots, are compared based on mobility, adaptability, energy efficiency, and inspection performance. The paper also proposes an autonomous wheeled inspection robot using sensor fusion and computer vision for obstacle avoidance, wireless communication, and autonomous navigation. Results show that autonomous inspection robots improve safety, fault detection, and inspection efficiency compared to manual methods. Challenges such as power consumption, communication delays, localization errors, and sensor calibration are discussed. Future developments involving AI, IoT, cloud robotics, edge computing, swarm robotics, and digital twins are expected to enhance intelligent infrastructure monitoring systems.
Pooja Agarwal, Rakesh Chandra· International Journal of Int...· 0 citations
The proposed AFCS-CRM significantly improves force tracking accuracy, manipulation stability, grasp reliability, response time, energy efficiency, and human safety, and demonstrates strong potential for next-generation smart manufacturing, robotic assembly, precision surgery, warehouse automation, and assistive robotics.
Pooja Agarwal, Rakesh Chandra· International Journal of Int...· 0 citations
Overall, intelligent robotics can significantly reduce human exposure, improve inspection quality, and enable early fault detection, while future research must focus on certifiable AI, resilient perception, and standardized benchmarking in hazardous environments.
Pooja Agarwal, Rakesh Chandra· International Journal of Int...· 0 citations