Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow

Accurate fall detection for the assistance of older people is crucial to reduce incidents of deaths or injuries due to falls. Meanwhile, a vision-based fall detection system has shown some significant results to detect falls. Still, numerous challenges need to be resolved. The impact of deep learning has changed the landscape of the vision-based system, such as action recognition. The deep learning technique has not been successfully implemented in vision-based fall detection systems due to the requirement of a large amount of computation power and the requirement of a large amount of sample training data. This research aims to propose a vision-based fall detection system that improves the accuracy of fall detection in some complex environments such as the change of light condition in the room. Also, this research aims to increase the performance of the pre-processing of video images. The proposed system consists of the Enhanced Dynamic Optical Flow technique that encodes the temporal data of optical flow videos by the method of rank pooling, which thereby improves the processing time of fall detection and improves the classification accuracy in dynamic lighting conditions. The experimental results showed that the classification accuracy of the fall detection improved by around 3% and the processing time by 40 to 50ms. The proposed system concentrates on decreasing the processing time of fall detection and improving classification accuracy. Meanwhile, it provides a mechanism for summarizing a video into a single image by using a dynamic optical flow technique, which helps to increase the performance of image pre-processing steps.Comment: 16 page

Unfamiliar technology: Reaction of international students to blended learning

This study enquires into learners’ behavioural intentions towards the use of a blended learning program designed for post-graduate international IT students. The aim of this research is to develop a testing mechanism to measure the extent to which international students have built up digital capital. We use the unified theory of acceptance and use of technology (UTAUT) as a framework for this investigation, built around social influence (behavioural intention) performance and effort expectancy (attitude), and facilitating conditions (ease and usefulness), using a critical theoretical approach. We further attempt to understand whether motivation for engagement with blended learning comes from intrinsic or extrinsic sources. For this study, 95 Project Management students were introduced to a blended learning approach using Blackboard, a Learning Management System. Following an introductory session, data on attitude, social influence and facilitating conditions was gathered. Hierarchical multiple regressions were used to assess the influence of each variable in determining first behavioural intentions and latter attitude towards blended learning. This study contributes to the body of knowledge through identifying that social influence has a strong impact on both performance and effort expectation as well as behavioural intentions. This suggests that, overall, the social environments from which the cohort originated provided sufficient economic, social and cultural capital to also develop some digital capita

Customer data extraction techniques based on natural language processing for e-commerce business analytics

Natural language processing (NLP) is the a types of artificial intelligence approach used to maintain the decision making and data interaction process with high accuracy and reliability rate. It is also used to maintain the computer-human interaction for better understanding and result. The aim of this work is to review the data extraction techniques with NLP for a better business and user analysis process. For data analysis and user experience analysis process data analytic, K-neighbor techniques are used that are obtained using the method a lLiterature review. This process aims to review the current research articles that are focused on data extraction and analytic techniques. Besides, it is focused on NLP techniques for improving the analysis and extraction process. The Factorization, FCMA, and soft computing algorithms with NLP are reviewed that maintain precision and accuracy rate. Different tools, such as visualization, decision-making, consumer identification, and behavior analysis, are considered during the review process. In this review process, PRM and embedding matrix approaches are considered for an accurate analysis process. The data extraction, feature extraction, and machine learning model with data extraction techniques are reviewed to manage consumer experience and error estimation. This study introduces customer behavior data, Natural processing-based data extraction, e-commerce business effectiveness and evaluation as the major factors of this work

Using deep learning for network traffic prediction to secure software networks against DDoS attacks

Deep learning (DL) is an emerging technology that is being used in many areas due to its effectiveness. One of its major applications is attack detection and prevention of backdoor attacks. Sampling-based measurement approaches in the software-defined network of an Internet of Things (IoT) network often result in low accuracy, high overhead, higher memory consumption, and low attack detection. This study aims to review and analyse papers on DL-based network prediction techniques against the problem of Distributed Denial of service attack (DDoS) in a secure software network. Techniques and approaches have been studied, that can effectively predict network traffic and detect DDoS attacks. Based on this review, major components are identified in each work from which an overall system architecture is suggested showing the basic processes needed. Major findings are that the DL is effective against DDoS attacks more than other state of the art approaches

A systematic review : machine learning based recommendation systems for e-learning

The constantly growing offering of online learning materials to students is making it more difficult to locate specific information from data pools. Personalization systems attempt to reduce this complexity through adaptive e-learning and recommendation systems. The latter are, generally, based on machine learning techniques and algorithms and there has been progress. However, challenges remain in the form of data-scarcity, cold-start, scalability, time consumption and accuracy. In this article, we provide an overview of recommendation systems in the e-learning context following four strands: Content-Based, Collaborative Filtering, Knowledge-Based and Hybrid Systems. We developed a taxonomy that accounts for components required to develop an effective recommendation system. It was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components. This paper makes a significant contribution to the field by providing a much-needed overview of the current state of research and remaining challenges

Unfamiliar technology : reaction of international students to blended learning

This study enquires into learners’ behavioural intentions towards the use of a blended learning program designed for post-graduate international IT students. The aim of this research is to develop a testing mechanism to measure the extent to which international students have built up digital capital. We use the unified theory of acceptance and use of technology (UTAUT) as a framework for this investigation, built around social influence (behavioural intention) performance and effort expectancy (attitude), and facilitating conditions (ease and usefulness), using a critical theoretical approach. We further attempt to understand whether motivation for engagement with blended learning comes from intrinsic or extrinsic sources. For this study, 95 Project Management students were introduced to a blended learning approach using Blackboard, a Learning Management System. Following an introductory session, data on attitude, social influence and facilitating conditions was gathered. Hierarchical multiple regressions were used to assess the influence of each variable in determining first behavioural intentions and latter attitude towards blended learning. This study contributes to the body of knowledge through identifying that social influence has a strong impact on both performance and effort expectation as well as behavioural intentions. This suggests that, overall, the social environments from which the cohort originated provided sufficient economic, social and cultural capital to also develop some digital capital

The effect of culture and belief systems on students’ academic buoyancy

Increasingly, learner motivation is implicated in student failure at universities. This has led to intense research into internal or external variables that might buoy resilience to failure. This research investigates the impact of strong cultural connectedness and strong belief systems on the academic buoyancy of international students studying at an Australian University. For this purpose, we surveyed 102 tertiary students at a Sydney university. Results demonstrate that, in the event of academic failure, students’ academic buoyancy remains high if they have support through strong cultural connections and from their belief systems. We further endeavored to identify if academic buoyancy was fed by intrinsic or extrinsic motivation. This research contributes to the understanding of the sources of strengths available to international students from primarily collective cultures studying overseas. There are implications for educational practice in terms of identifying students ‘at high risk’ if they are unable to draw strengths from cultural connections and belief systems

Rise of Social Media Marketing: A Perspective on Higher Education

This research aims to examine the impact of social media on higher education marketing, in terms of student recruitment. Based on a review of secondary literature, this paper proposes a framework for student recruitment. The expected results indicated that there are several approaches that marketers could use to reach and recruit prospective students more effectively. These approaches include positive electronic word-of-mouth (e-WOM), social media campaigns, virtual campus tours and Facebook Live, which have significant and positive impact on student recruitment. Moreover, the study suggests that social media validation plays a mediating role between social media campaigns and student recruitment. This study aids institutions in gaining insights into students' behaviours, needs, and attitudes towards social media marketing. Furthermore, it provides admissions staff guidance on how to use social media communities as an effective recruitment tool - particularly Facebook

Deep learning for aspect-based sentiment analysis : a comparative review

The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering, as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context

Object detection and recognition : using deep learning to assist the visually impaired

Background: Deep learning systems have improved performance of devices through more accurate object detection in a significant number of areas, for medical aid in general, and also for navigational aids for the visually impaired. Systems addressing different needs are available, and many manage effectively the detection of static obstacles. Purpose: This research provides a review of deep learning systems used with navigational tools for the visually Impaired and a framework for guidance for future research. Methods: We compare current deep learning systems used with navigational tools for the visually impaired and compile a taxonomy of indispensable features for systems. Results: Challenges to detection. Our taxonomy of improved navigational systems shows that it is sufficiently robust to be generally applied. Conclusion: This critical analysis is, to the best of our knowledge, the first of its kind and will provide a much-needed overview of the field.Implication for Rehabilitation Deep learning systems can provide lost cost solutions for the visually impaired. Of these, convolutional neural networks (CNN) and fully convolutional neural networks (FCN) show great promise in terms of the development of multifunctional technology for the visually impaired (i.e., being less specific task oriented). CNN have also potential for overcoming challenges caused by moving and occluded objects. This work has also highlighted a need for greater emphasis on feedback to the visually impaired which for many technologies is limited