MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Automating human activity recognition is one of computer vision's most appealing and pragmatic research areas. In this article, we have addressed the problem of video-based student activity detection. The student’s activity detection using YOLO (SADY) aims to recognize the normal and abnormal student activities to ensure immediate intervention in case of any risk or necessity. We created our classroom data set of around 220 recordings depicting seven student classroom activities. The YOLOv4 Tiny model was retrained using 5000 labeled keyframes extracted from the train videos. The model was then tested for single or multiple activity detections. We presented the evaluated results for various values of hyperparameters like confidence threshold and Intersection Over Union (IoU) thresholds for the proposed model. The model assigns a unique confidence score and action label to each frame for the test videos by positioning recurrent activity labels. The proposed approach achieved a mean average precision (mAP) of 95% and a frame per second rate (FPS) of 45 for the student activity Class Room (CR) dataset and mAP of 95.18 % for the LIRIS dataset. The experimental findings using the Class Room recorded and LIRIS publicly accessible dataset show that our proposed approach outperforms existing approaches regarding recognition accuracy and speed. The comparable results obtained in this research work imply that the proposed framework could effectively monitor student’s activities in schools, colleges, and universities. © IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

School of Electronics & Communication Engineering

Engineering and Technology

World University root node

International Journal on Advanced Science, Engineering and Information Technology

SADY: Student Activity Detection Using YOLO-based Deep Learning Approach

This article proposes active learning strategies for delivering engineering courses to the students in order to make learning more engaging, interesting, accomplishing, and joyful. The education system was impacted by the unprecedented situations generated by the Coronavirus pandemic from the last two academic years. The student community is also going through difficult situations on many levels. The primary challenge for teachers was to engage the students passionately in learning while maintaining a stress-free, healthy and fruitful environment in the online classes. For a stress-free teaching-learning process, adopting a variability in course content delivery methods would be a smart choice for teachers. This paper discusses the most effective active learning strategies in higher education. The work also highlights examples of implementation and an analysis of the effectiveness of active learning methodologies in terms of learners understanding, competency, satisfaction and knowledge gain. The findings indicate that the active learning strategy has assisted students in being more comfortable and engaged in effective learning in the online mode. © 2022, Rajarambapu Institute Of Technology. All rights reserved.

Journal of Engineering Education Transformations

Active Learning Techniques for Effective Online Teaching and Learning in Higher Education

Lately, human activity recognition has pulled in an expanding measure of consideration from research and industry networks. Human activity recognition is the consistently sprouting exploration zone as it finds magnificent utilizations in surveillance, healthcare, and many real-life problems. This paper presents a technique to automatically recognize human activity from a video sequence. An integrated features approach using Histogram of Gradient (HOG) local feature descriptor and Principal Component Analysis (PCA) as a global feature is proposed in this paper. Optimized Support Vector Machine(SVM), Artificial Neural Network (ANN) used as a classifier. The proposed model is trained and tested on Benchmark KTH dataset, results obtained are comparable with existing methods. The proposed technique achieved the activity recognition accuracy of 99.21 percent. The experimental results confirms that the embedded feature approach and optimization techniques for classifier improves the performance of human activity recognition © 2021 IEEE.

2021 International Conference on Emerging Smart Computing and Informatics, ESCI 2021

An improved model for human activity recognition by integrated feature approach and optimized SVM

Human activity recognition framework is facing many challenges and issues that are promoting the development of a new activity recognition system to improve accuracy under realistic conditions. Therefore, efficient human activity detection is proposed in this paper. At first, the input video is converted into frames then the keyframes are selected using a structural similarity measure. Next, the local and global features are selected from the video frames. Finally, the selected features are fed to the classifier for identifying human activity. In the proposed method, the traditional deep learning algorithm is improved by applying optimization techniques. Adaptive Monarch Butterfly Optimization algorithm (AMBO) is used to improve the performance of the traditional deep learning algorithm. Monarch Butterfly Optimization Algorithm (MBO) is a population-based natural inspired algorithm. It mimics the foraging and the social behavior of the butterflies. The optimal parameters are selected for activity detection. The proposed method is computed based on accuracy, sensitivity, and specificity and implemented using the MATLAB platform. The experimental results show activity recognition accuracy improved to 96%. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.