Image Segmentation and Classification using Small Data: An Application on Friendly Statements - A Computer Vision and Document Intelligence Project

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceInsurance companies face significant challenges in managing numerous physical documents containing critical information, resulting in considerable time and cost expenditures. Although Deep Learning models offer a promising solution, their implementation costs and data privacy concerns restrict widespread adoption, especially when dealing with confidential documents. This internship report presents a novel approach to address these challenges by developing a lightweight computer vision solution for accurately detecting and processing checkboxes from Portuguese friendly statements. The key objective was to demonstrate the feasibility of achieving high accuracy without relying on advanced Deep Learning techniques. By leveraging a small set of examples, we successfully extracted checkbox information while mitigating the high computational requirements associated with traditional Deep Learning models. The results highlight the practicality and cost-effectiveness of our approach, offering insurance companies a viable solution to streamline document management, enhance data security, and improve overall efficiency. This research contributes to the computer vision field by providing valuable insights into alternative methodologies that can be adopted to overcome the limitations of Deep Learning, facilitating broader accessibility and utilization among insurance providers

Development and evaluation of a haptic framework supporting telerehabilitation robotics and group interaction

Telerehabilitation robotics has grown remarkably in the past few years. It can provide intensive training to people with special needs remotely while facilitating therapists to observe the whole process. Telerehabilitation robotics is a promising solution supporting routine care which can help to transform face-to-face and one-on-one treatment sessions that require not only intensive human resource but are also restricted to some specialised care centres to treatments that are technology-based (less human involvement) and easy to access remotely from anywhere. However, there are some limitations such as network latency, jitter, and delay of the internet that can affect negatively user experience and quality of the treatment session. Moreover, the lack of social interaction since all treatments are performed over the internet can reduce motivation of the patients. As a result, these limitations are making it very difficult to deliver an efficient recovery plan. This thesis developed and evaluated a new framework designed to facilitate telerehabilitation robotics. The framework integrates multiple cutting-edge technologies to generate playful activities that involve group interaction with binaural audio, visual, and haptic feedback with robot interaction in a variety of environments. The research questions asked were: 1) Can activity mediated by technology motivate and influence the behaviour of users, so that they engage in the activity and sustain a good level of motivation? 2) Will working as a group enhance users’ motivation and interaction? 3) Can we transfer real life activity involving group interaction to virtual domain and deliver it reliably via the internet? There were three goals in this work: first was to compare people’s behaviours and motivations while doing the task in a group and on their own; second was to determine whether group interaction in virtual and reala environments was different from each other in terms of performance, engagement and strategy to complete the task; finally was to test out the effectiveness of the framework based on the benchmarks generated from socially assistive robotics literature. Three studies have been conducted to achieve the first goal, two with healthy participants and one with seven autistic children. The first study observed how people react in a challenging group task while the other two studies compared group and individual interactions. The results obtained from these studies showed that the group interactions were more enjoyable than individual interactions and most likely had more positive effects in terms of user behaviours. This suggests that the group interaction approach has the potential to motivate individuals to make more movements and be more active and could be applied in the future for more serious therapy. Another study has been conducted to measure group interaction’s performance in virtual and real environments and pointed out which aspect influences users’ strategy for dealing with the task. The results from this study helped to form a better understanding to predict a user’s behaviour in a collaborative task. A simulation has been run to compare the results generated from the predictor and the real data. It has shown that, with an appropriate training method, the predictor can perform very well. This thesis has demonstrated the feasibility of group interaction via the internet using robotic technology which could be beneficial for people who require social interaction (e.g. stroke patients and autistic children) in their treatments without regular visits to the clinical centres

From a simple EHR to the market lead: what technologies to add

Electronic health records (EHRs) can store, capture, and present patient data in an organized way that improves physicians’ workflow and patient care. This makes EHRs key to addressing many of today’s health care challenges. An interdisciplinary review and qualitative study of artificial intelligence, machine learning, natural language processing, and real-time location services in health care was conducted. The results show that in an industry where digitization is key, several recommendations can be made to leverage these technologies in ways that can improve current systems and help EHR vendors become the market lead

The application of a business intelligence tool for service delivery improvement : the case of South Africa

Abstract: The global environment requires organisations to adapt and respond quickly to the complexity of its nature. Responding to such an environment depends on real-time information. In the last decade, organisations have relied much on human expertise to extract and analyse and process data into meaningful information for decision making. Many will probably agree with the assertion that the complexity of the globalisation has led to a complexity in modern data analysis, which encompasses different elements (technology and innovation, internet of things and influx of data to name but few), resulting in modern scientific problems. It is evident that organisational knowledge has become the enabling factor for decision-making in both the private and public sector. Yet, the study of the opinion that the advancement of technology and internet of things has complicated matters further for humankind to interpret complex and vast amounts of data at the speed required to keep up with the demands of the global environment in which they operate. Therefore, it is likely that the discovered knowledge may be inaccurate at times. In responding to these dynamics, organisations require computational intelligence systems to transform the data they acquire into real-time meaningful information in order to make informed decisions. ..D.Phil. (Engineering Management

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 153)

This bibliography lists 175 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1976

Human Recognition Theory and Facial Recognition Technology: A Topic Modeling Approach to Understanding the Ethical Implication of a Developing Algorithmic Technologies Landscape on How We View Ourselves and Are Viewed by Others

The emergence of algorithmic-driven technology has significantly impacted human life in the current century. Algorithms, as versatile constructs, hold different meanings across various disciplines, including computer science, mathematics, social science, and human-artificial intelligence studies. This study defines algorithms from an ethical perspective as the foundation of an information society and focuses on their implications in the context of human recognition. Facial recognition technology, driven by algorithms, has gained widespread use, raising important ethical questions regarding privacy, bias, and accuracy. This dissertation aims to explore the impact of algorithms on machine perception of human individuals and how humans perceive one another and themselves. By analyzing publications from the National Institute of Standards and Technology (NIST) and employing topic modeling, this research identifies the ethical themes surrounding facial recognition technology. The findings contribute to a broader understanding of the ethical implications of algorithms in shaping human perception and interaction, with a focus on the multidimensional aspects of human recognition theory. The research also examines the ethical considerations in AI-AI interactions, human-AI interactions, and humans perceiving themselves in the context of facial recognition technology. The study establishes a framework of human recognition theory that encompasses the alteration and reshaping of fundamental human values and self-perception, highlighting the transformative effects of algorithmic-driven technologies on human identity and values. The dissertation chapters provide a comprehensive overview of the influence of AI on societal values and identity, the revolution of big data and Information and Communication Technology (ICT), the concept of digital identity in the fourth industrial revolution, and recognition theory in the era of algorithms. The research aims to inform discussions and policy decisions regarding the responsible development and deployment of algorithms in recognition processes, addressing the challenges and opportunities brought about by algorithmic systems in shaping human recognition, identity, and the social fabric of our increasingly algorithmic society