1,576 research outputs found
Regulating Mobile Mental Health Apps
Mobile medical apps (MMAs) are a fastâgrowing category of software typically installed on personal smartphones and wearable devices. A subset of MMAs are aimed at helping consumers identify mental states and/or mental illnesses. Although this is a fledgling domain, there are already enough extant mental health MMAs both to suggest a typology and to detail some of the regulatory issues they pose. As to the former, the current generation of apps includes those that facilitate selfâassessment or selfâhelp, connect patients with online support groups, connect patients with therapists, or predict mental health issues. Regulatory concerns with these apps include their quality, safety, and data protection. Unfortunately, the regulatory frameworks that apply have failed to provide coherent riskâassessment models. As a result, prudent providers will need to progress with caution when it comes to recommending apps to patients or relying on appâgenerated data to guide treatment
Data Science and Knowledge Discovery
Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining
Designing a Chatbot-Enabled Laptop Diagnostic Assistant
This paper proposes a chatbot developed with deep learning techniques to help people troubleshoot operating system errors in laptops. In today's world, people can't wait for anything and expect an immediate response when they have a question because they want their problems solved quickly and completely. The system addresses the software aspect of technical laptop issues concerning a laptop's operating system. Deep learning is used to create the chatbot because it has been shown to be more accurate in selecting its response when conversing with users. The chatbot will be integrated into Telegram, an instant messaging service, and users will be able to communicate about laptop issues via Telegram
Application of Text Analytics in Public Service Co-Creation: Literature Review and Research Framework
The public sector faces several challenges, such as a number of external and
internal demands for change, citizens' dissatisfaction and frustration with
public sector organizations, that need to be addressed. An alternative to the
traditional top-down development of public services is co-creation of public
services. Co-creation promotes collaboration between stakeholders with the aim
to create better public services and achieve public values. At the same time,
data analytics has been fuelled by the availability of immense amounts of
textual data. Whilst both co-creation and TA have been used in the private
sector, we study existing works on the application of Text Analytics (TA)
techniques on text data to support public service co-creation. We
systematically review 75 of the 979 papers that focus directly or indirectly on
the application of TA in the context of public service development. In our
review, we analyze the TA techniques, the public service they support, public
value outcomes, and the co-creation phase they are used in. Our findings
indicate that the TA implementation for co-creation is still in its early
stages and thus still limited. Our research framework promotes the concept and
stimulates the strengthening of the role of Text Analytics techniques to
support public sector organisations and their use of co-creation process. From
policy-makers' and public administration managers' standpoints, our findings
and the proposed research framework can be used as a guideline in developing a
strategy for the designing co-created and user-centred public services
Natural Language Processing Applications in Business
Increasing dependency of humans on computer-assisted systems has led to researchers focusing on more effective communication technologies that can mimic human interactions as well as understand natural languages and human emotions. The problem of information overload in every sector, including business, healthcare, education etc., has led to an increase in unstructured data, which is considered not to be useful. Natural language processing (NLP) in this context is one of the effective technologies that can be integrated with advanced technologies, such as machine learning, artificial intelligence, and deep learning, to improve the process of understanding and processing the natural language. This can enable human-computer interaction in a more effective way as well as allow for the analysis and formatting of large volumes of unusable and unstructured data/text in various industries. This will deliver meaningful outcomes that can enhance decision-making and thus improve operational efficiency. Focusing on this aspect, this chapter explains the concept of NLP, its history and development, while also reviewing its application in various industrial sectors
Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time
Crowd-powered conversational assistants have been shown to be more robust
than automated systems, but do so at the cost of higher response latency and
monetary costs. A promising direction is to combine the two approaches for high
quality, low latency, and low cost solutions. In this paper, we introduce
Evorus, a crowd-powered conversational assistant built to automate itself over
time by (i) allowing new chatbots to be easily integrated to automate more
scenarios, (ii) reusing prior crowd answers, and (iii) learning to
automatically approve response candidates. Our 5-month-long deployment with 80
participants and 281 conversations shows that Evorus can automate itself
without compromising conversation quality. Crowd-AI architectures have long
been proposed as a way to reduce cost and latency for crowd-powered systems;
Evorus demonstrates how automation can be introduced successfully in a deployed
system. Its architecture allows future researchers to make further innovation
on the underlying automated components in the context of a deployed open domain
dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human
Factors in Computing Systems 2018 (CHI'18
Automated information retrieval and services of graduate school using chatbot system
Automated information retrieval and servicing systems are a priority demand system in today's businesses to ensure instantaneous customer satisfaction. The chatbot system is an incredible technological application that enables communication channels to automatically respond to end-users in real-time and 24 hours a day. By providing effective services for retrieving information and electronic documents continuously and automating the information service system, the coronavirus disease (COVID-19) is challenging to promote graduate school programs, update news, and retrieve student information in this era. This article discusses automated information retrieval and services based on the architecture, components, technology, and experiment of chatbots. The chatbot system's primary functions are to deliver the course and contact information, answer frequency questions, and provide a link menu to apply for our online course platform. We manage the entire functional process of gathering course information and submitting an application for a course online. The final results compare end users' perceptions of chatbot system usage to onsite services to ensure that the chatbot system can be integrated into the university's information system, supporting university-related questions and answers. We may expand our chatbot system's connection to the university's server to provide information services to students in various informative areas for future research
Milchbot: App to Support the Process of Feeding and Caring for Dairy Cows in Peru
At present, Peru's agricultural sector has a shortfall of professionals, so livestock producers cannot be provided with relevant and reliable information to ensure good nutrition and care for dairy cows, which affects productivity. Milchbot is a chatbot that answers queries about the feeding and care of dairy cows based on reliable documentation. To do so, a chatbot model was designed to cover the topics of feeding, care, news and frequently asked questions for the planning, feeding and care processes about dairy cows. The model consists of a friendly interface, a dialog engine and a search engine that allows you to find and provide information from a document storage. This model was implemented employing Watson Assistant and Discovery. Milchbot was used and evaluated by 6 livestock producers and 7 zootechnicians. The results of the usability and satisfaction surveys show a high rating for both livestock producers and zootechnicians, and it should be noted that zootechnicians gave very high ratings on satisfaction.New York Universit
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