Muhammad Farhan

Big-data: transformation from heterogeneous data to semantically-enriched simplified data

In big data, data originates from many distributed and different sources in the shape of audio, video, text and sound on the bases of real time; which makes it massive and complex for traditional systems to handle. For this, data representation is required in the form of semantically-enriched for better utilization but keeping it simplified is essential. Such a representation is possible by using Resource Description Framework (RDF) introduced by World Wide Web Consortium (W3C). Bringing and transforming data from different sources in different formats into the RDF form having rapid ratio of increase is still an issue. This requires improvements to cover transition of information among all applications with induction of simplicity to reduce complexities of prominently storing data. With the improvements induced in the shape of big data representation for transformation of data to form into Extensible Markup Language (XML) and then into RDF triple as linked in real time. It is highly needed to make transformation more data friendly. We have worked on this study on developing a process which translates data in a way without any type of information loss. This requires to manage data and metadata in such a way so they may not improve complexity and keep the strong linkage among them. Metadata is being kept generalized to keep it more useful than being dedicated to specific types of data source. Which includes a model explaining its functionality and corresponding algorithms focusing how it gets implemented. A case study is used to show transformation of relational database textual data into RDF, and at end results are being discussed.

Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning

Abstract Multimedia is an essential and integral part of electronic learning (e-learning). In this study, teaching performance and student learning experience are measured using real-time multimedia processing tools and techniques for the e-learning paradigm. Visual attention and visual engagement analysis are performed using two developed algorithms. Video lectures are recorded and delivered to students in e-learning pedagogical setup, which are examined for the visual attention and visual engagement of the student and teacher, respectively. Proposed methodology integrates the assessment on both student and teacher ends. Multimedia processing of video lectures for teaching performance produces scoring dataset. The same methodology on student end for visual attention is used to investigate student experience. These types of datasets then reduced to time-based datasets from the image-based dataset. Correlation and association of both datasets provide the opportunity to relate both student experience and teaching performance as well as to move forward to create content that is more useful. Computational performance of the developed algorithms is compared using different video lectures with their processed frames per second, which is analyzed as per their corresponding bins. Mean, max, and median of the processed frames of all the processed videos are also compared.

Multiagent Semantical Annotation Enhancement Model for IoT-Based Energy-Aware Data

The Internet of Things (IoT) is involved in dealing with physical items, gadgets, vehicles, structures, and different things that are inserted into hardware, programming, sensors, and system availability, which empowers these items to gather and trade information. Improving extraction of sensor-based data for energy awareness and then annotating it and converting it into semantically enabled form for analyzing results with the use of improved tools and applications are the focus of this research. However, as the amount of real time data gets huge, it becomes difficult to track results when needed at once. Reconciliation of heterogeneous information sources into an interlinked data is a standout among the most pertinent difficulties for some learning based systems these days. This paper forms suitable elements by a methodology for adjustment of heterogeneous sensor-based Web assets, where different tools and applications like weather detection for self-observing and self-diagnostics use dispersed human specialists and learning. The proposed general model uses a capability of the Semantic Web innovation and concentrates on the part of a semantic adjustment of existing broadly utilized models of information representation to Resource Description Framework (RDF) based semantically rich arrangement. This work is valuable for sorting out and inquiry of the detecting information in the Internet of Things.

Multimedia based qualitative assessment methodology in eLearning: student teacher engagement analysis

Multimedia-based Electronic learning (eLearning) is an effective method of knowledge transfer. Multimedia-based eLearning provides the opportunity to the students that they can use once delivered and recorded video lecture any time. Multimedia tools and its applications if used in eLearning enable the students and teachers to take such kind of advantages. Multimedia applications provide the advantages to students and teacher in eLearning but challenges are also striking features. One of the challenges is to measure and analyze visual engagement and visual attention for teachers and students respectively. Visual engagement of the video lecture is measured and analyzed to verify the effectiveness of the teachers’ lectures. In the same way, the visual attention for the students is also be measured and analyzed. We have presented a qualitative assessment methodology and an algorithm for both of the measures and their correlation. Covariance and relationship between the two measures are the key technique of this paper. Multimedia-based software tools have been developed for this purpose. Results of both the tools are very interesting and promising. They are interpreted using descriptive and graphical statistics.

IoT-based students interaction framework using attention-scoring assessment in eLearning

Students’ interaction and collaboration using Internet of Things (IoT) based interoperable infrastructure is a convenient way. Measuring student attention is an essential part of educational assessment. As new learning styles develop, new tools and assessment methods are also needed. The focus of this paper is to develop IoT-based interaction framework and analysis of the student experience of electronic learning (eLearning). The learning behaviors of students attending remote video lectures are assessed by logging their behavior and analyzing the resulting multimedia data using machine learning algorithms. An attention-scoring algorithm, its workflow, and the mathematical formulation for the smart assessment of the student learning experience are established. This setup has a data collection module, which can be reproduced by implementing the algorithm in any modern programming language. Some faces, eyes, and status of eyes are extracted from video stream taken from a webcam using this module. 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 take student learning behaviors into account when assessing electronic learning strategies.

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.

LSA Based Smart Assessment Methodology for SDN Infrastructure in IoT Environment

The Software Defined Network (SDN) is merged in the Internet of Things (IoT) to interconnect large and complex networks. It is used in the education system to interconnect students and teacher by heterogenous IoT devices. In this paper, the SDN-based IoT model for students’ Interaction is proposed which interconnects students to a teacher in a smart city environment. The students and teachers are free to move to anywhere, anytime and with any hardware. An architecture model for students’ teacher’s interaction in IoT is proposed which shows the details procedure about the interaction of teacher with students for electronic assessment. The SDN solves the scalability and interoperability issues between their heterogenous IoT devices. A Methodology for Students’ Answer Assessment using Latent Semantic Analysis (LSA) is proposed which calculates the semantic similarity between teacher’s question and students’ answers. The LSA is used to calculate semantic similarity between text documents. It is used to mark the students’ answers automatically by semantics. The Students’ can see results through their IoT devices just after finishing the examination with more accurate marks We have collected fifty (50) undergraduate students’ data from Learning Management System (LMS) of Virtual University (VU) of Pakistan. The experiment is implemented on eighteen (18) students’ answers in R Studio with R version 3.4.2. Teachers are provided with four (4) bins of the mark while the proposed method assigns accurate marks. The experimental results show that the proposed methodology gave accurate results as compared to teacher’s marks.

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.

Design of an IoT based warfare car robot using sensor network connectivity

Robots remain the focus of researchers and developers, and now they are moving towards IoT based devices and mobile robots to take advantage of the different sensor enables facilities. A robot is a machine capable of carrying out a complex series of actions automatically, especially one programmable by a computer. A robot can be controlled by a human and can be modified by its functionality at runtime by the operator. From past few decades, researchers are contributing towards Robotics. There is no end of technology, creativity, and innovation. The project is designed to develop a robot using android application for remote operation attached to the wireless camera for monitoring purpose. Surveillance using the camera can help the soldier team to make strategies at run-time. This kind of robot can be helpful for spying purpose in war fields. The android application loaded on mobile devices can connect to the security system and easy to use GUI and visualization of the Warfield. The security system then acts on these commands and responds to the user. The camera and the motion detector are attached to the system for remote surveillance using wireless protocol 802.11, ZigBee and Bluetooth protocols. This robot is having the functionality of mines detection, object detection, GPS used for location and navigation and a gun to fire the enemy at the runtime.

IoT based smart interaction framework for elearning

Multimedia content boosts the learning trends. This Multimedia and Agents based Question Answering System (MAQAS) is based on Internet of Things in eLearning paradigm. It is aimed to give help especially to the students in finding the more relevant and accurate answers to their questions. This system is for both synchronous and asynchronous communications. The multimedia-based IoT-centric environment is suitable to enhance the effectiveness of the delivery of learning contents. This creates a flexible eLearning paradigm for the teachers and students. The proposed eLearning model uses sensors to detect the student location, temperature, and mobile camera to identify the students presence at a suitable place, inappropriate atmosphere and his activeness in a learning environment, respectively. Three agents are playing vital roles in making the smart decision about student-teacher interactions. Experimentation is performed and the initial results are drawn. Accuracy value of MAQAS is compared with the well-known existing QA systems; LIVE QA TRACK, QUORA, YODA QA LIVE, AND ASKMSR-QA concerning three types of WH Questions. The overall performance of the proposed systems is proved to be better compared to other competing systems. It was also concluded that eLearning with multimedia settles the goodness of an IoT-based solution. It also enhances the QA to Multimedia and Agent-based Question Answering System.