Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 127, April 1974

This special bibliography lists 279 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1974

Machine learning methods for sign language recognition: a critical review and analysis.

Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts making automatic sign language recognition (ASLR) a complex system. In order to overcome such complexity, researchers are investigating better ways of developing ASLR systems to seek intelligent solutions and have demonstrated remarkable success. This paper aims to analyse the research published on intelligent systems in sign language recognition over the past two decades. A total of 649 publications related to decision support and intelligent systems on sign language recognition (SLR) are extracted from the Scopus database and analysed. The extracted publications are analysed using bibliometric VOSViewer software to (1) obtain the publications temporal and regional distributions, (2) create the cooperation networks between affiliations and authors and identify productive institutions in this context. Moreover, reviews of techniques for vision-based sign language recognition are presented. Various features extraction and classification techniques used in SLR to achieve good results are discussed. The literature review presented in this paper shows the importance of incorporating intelligent solutions into the sign language recognition systems and reveals that perfect intelligent systems for sign language recognition are still an open problem. Overall, it is expected that this study will facilitate knowledge accumulation and creation of intelligent-based SLR and provide readers, researchers, and practitioners a roadmap to guide future direction

PROGRAM EVALUATION OF READING PLUS : STUDY OF THE IMPACT ON READING ACHIEVEMENT FOR NINTH-GRADE STUDENTS IN MOORE COUNTY SCHOOLS

The following is a program evaluation of the Reading Plus program in Moore County Schools from southern North Carolina and its impact on reading achievement for ninth-grade students. Reading Plus is a valuable asset that may be utilized to increase student ability to read. The Reading Plus program increases the degree of phonemic awareness, phonics, fluency, reading stamina, comprehension, character recognition and recall, vocabulary acquisition skills, and Lexile levels of students when the program is implemented with fidelity. The results of this study show that students who spent sufficient time on task, with each component of the program, demonstrated growth in all the areas mentioned above. Teachers who implemented this pilot were overwhelmingly pleased with the student outcomes and the program itself. The outcomes of this program evaluation were so impressive that the Reading Plus program is now being implemented at Riverside High School in Durham Public Schools, located in central North Carolina

Inclusive Intelligent Learning Management System Framework

Machado, D. S-M., & Santos, V. (2023). Inclusive Intelligent Learning Management System Framework. International Journal of Automation and Smart Technology, 13(1), [2423]. https://doi.org/10.5875/ausmt.v13i1.2423The article finds context and the current state of the art in a systematic literature review on intelligent systems employing PRISMA Methodology which is complemented with narrative literature review on disabilities, digital accessibility and legal and standards context. The main conclusion from this review was the existing gap between the available knowledge, standards, and law and what is put into practice in higher education institutions in Portugal. Design Science Research Methodology was applied to output an Inclusive Intelligent Learning Management System Framework aiming to help higher education professors to share accessible pedagogic content and deliver on-line and presential classes with a high level of accessibility for students with different types of disabilities, assessing the uploaded content with Web content Accessibility Guidelines 3.0, clustering students according to their profile, conscient feedback and emotional assessment during content consumption, applying predictive models and signaling students at risk of failing classes according to study habits and finally applying a recommender system. The framework was validated by a focus group to which experts in digital accessibility, information systems and a disabled PhD graduate.publishersversionpublishe

Personalization in object-based audio for accessibility : a review of advancements for hearing impaired listeners

Hearing loss is widespread and significantly impacts an individual’s ability to engage with broadcast media. Access can be improved through new object-based audio personalization methods. Utilizing the literature on hearing loss and intelligibility this paper develops three dimensions which are evidenced to improve intelligibility: spatial separation, speech to noise ratio and redundancy. These can be personalized, individually or concurrently, using object based audio. A systematic review of all work in object-based audio personalization is then undertaken. These dimensions are utilized to evaluate each project’s approach to personalisation, identifying successful approaches, commercial challenges and the next steps required to ensure continuing improvements to broadcast audio for hard of hearing individuals

Real-time New Zealand sign language translator using convolution neural network

Over the past quarter of a century, machine Learning performs an essential role in information technology revolution. From predictive internet web browsing to autonomous vehicles; machine learning has become the heart of all intelligence applications in service today. Image classification through gesture recognition is sub field which has benefited immensely from the existence of this machine learning method. In particular, a subset of Machine Learning known as deep learning has exhibited impressive performance in this regard while outperforming other conventional approaches such as image processing. The advanced Deep Learning architectures come with artificial neural networks particularly convolution neural networks (CNN). Deep Learning has dominated the field of computer vision since 2012; however, a general criticism of this deep learning method is its dependence on large datasets. In order to overcome this criticism, research focusing on discovering data- efficient deep learning methods have been carried out. The foremost finding of the data-efficient deep learning function is a transfer learning technique, which is basically carried out with pre-trained networks. In this research, the InceptionV3 pre trained model has been used to perform the transfer learning method in a convolution neural network to implement New Zealand sign language translator in real-time. The focus of this research is to introduce a vision-based application that offers New Zealand sign language translation into text format by recognizing sign gestures to overcome the communication barriers between the deaf community and hearing-unimpaired community in New Zealand. As a byproduct of this research work, a new dataset for New Zealand sign Language alphabet has been created. After training the pre-trained InceptionV3 network with this captured dataset, a prototype for this New Zealand sign language translating system has been created

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by Internet of Things (IoT) devices and applies Artificial Intelligence models, specifically, machine learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims to identify the machine-learning models used across different research on Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, the IoT devices used, and gaps and opportunities for further development. The survey results show that 50% of the analyzed research address visual impairment, and, for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constitute the majority of IoT devices. Deep neural networks represent 81% of the machine-learning models applied in the reviewed research.N/