Muhammad Munwar Iqbal

Multimedia based student-teacher smart interaction framework using multi-agents in eLearning

Multimedia content comprises the graphics, audio & video clips, animation and text to present learning materials in a style, which improves learner expectation in eLearning paradigm. Electronic learning gained the popularity due to its immense coverage of students and subjects all over the world. The aim of this study is enhancements using agent-based framework through multimedia data in eLearning paradigm. Analysis of multimedia contents and eLearning data are helpful for the course designers, teachers, and administrators of eLearning environments to hunt for undetected patterns and underlying data in learning processes. This research improves the learning curves for the students. It also needs to improve the overall processes in eLearning paradigm. Information and Communication Technologies supported education, and virtual classrooms environments are mandatory. In eLearning data is evolving day by day that includes the semi-structured data, unstructured data, and structured data which is also collectively marked as multimedia big data. Multimedia data has the potential to mining for the analytics and learning. The learning outcomes for the students are very important to find the facts that what impacts the input data on the student. There are 1108 students posted questions in online Learning Management System (LMS) and instructors reply these queries. Sensor data is also gathered by the mobile GPS to find the student location. The system has analyzed the relevance of the replied answers. The student satisfaction is achieved by providing the multimedia-based student-teacher interaction. This can lead to synchronous communication and multimedia content conversation in eLearning paradigm. Machine learning techniques are applied to that data to discover the patterns and behavioral trends. It can also be used in the eLearning environments for the teacher to assist and enhance the pedagogical skills and for student’s learning curve enhancements.

Multimedia based IoT-centric smart framework for eLearning paradigm

Multimedia content boosts the learning trends. This paper is aimed to presents an electronic learning system based on Internet of Things (IoT) for the synchronous and asynchronous communications. The infrastructure of IoT provides the adaptable, scalable and open access for the eLearning paradigm. The multimedia-based IoT-centric environment is suitable to enhance the effectiveness of the delivery of learning contents. Students can take full advantage of 7As of IoT, which provides the opportunity to the students that they can access everything on the internet at any time and place. It creates a flexible eLearning paradigm for the teachers and students. The proposed eLearning modeluses sensors to detect the student location, temperature, and mobile camera to identify the student activeness in thelearning environment. Virtual campuses are controlled from a centralized location that may be called the head office. The MAQAS framework provides the solutions to the problems and analyzes the results for the efficient and connected eLearning paradigm. The MAQAS system is used to answer student’s queries, which are responded to automatically by agent-based question answering system. The results show that the students’ participation towards learning and teacher’s pedagogy are more efficient in synchronous and asynchronous modes. Performance evaluated by comparison to the existing question answering Live QA Trak, Quora Yoda QA Live and AskMSR-QA with MAQAS.

A Real-Time Data Mining Approach for Interaction Analytics Assessment: IoT Based Student Interaction Framework

Students’ interaction and collaboration with the fellows and teachers using the Internet of Things (IoT) based interoperable infrastructure is a convenient way. Measuring student attention is an essential part of the educational assessment for students’ interaction. As new learning styles develop, new tools and assessment methods are also needed. The focus in this paper is to develop IoT based interaction framework and analysis of the student experience in electronic learning (eLearning) so that the students can take full advantage of the modern interaction technology and their learning can increase to a high level. This setup has a data collection module, which is implemented using Visual C# programming language and computer vision library. The number of faces, number of eyes, and status of eyes are extracted from the video stream, which is taken from a video camera. The extracted information is saved in a dataset for further analysis. The analysis of the dataset produces interesting results for student learning assessments. Modern learning management systems can integrate the developed tool to consider student-learning behaviors when assessing electronic learning strategies. The tools are also developed for the data collection on both student and teacher ends. Correlation of data and hidden meaning are extracted to make the learning experience and teaching performance better and adaptable. IoT based infrastructure provides the facilities to fellow students about location awareness, fellows’ accessibility, social behavior and helping hand.

An enhanced framework for multimedia data: Green transmission and portrayal for smart traffic system

Abstract The object tracking in video surveillance for intelligent traffic handling in smart cities requires an enormous amount of data called big data to be transmitted over the network using the Internet of Things. Manual monitoring and surveillance are impossible because traditional computer vision technologies are no more useful for massive processing and intelligent decision making. In this paper, a framework is proposed which enables both on spot data processing and intelligent decision making by using cloud computing. The developed application is a trained on Artificial Neural Network, which can handle different traffic techniques with congested traffic scenario and priorities traffic such as ambulance handling. The Message Queue Telemetry Transport protocol is used for green transmission with mobile access to traffic data. The results analyzed with thirty videos processed data which handle real-time data prioritization for the people for smart surveillance to fastest route and enhance the intelligent data transmission.