scholarly journals Leveraging Artificial Intelligence to Improve Voice Disorder Identification Through the Use of a Reliable Mobile App

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 124048-124054 ◽  
Author(s):  
Laura Verde ◽  
Giuseppe De Pietro ◽  
Mubarak Alrashoud ◽  
Ahmed Ghoneim ◽  
Khaled N. Al-Mutib ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mobile App ◽  
Voice Disorder ◽  
Disorder Identification

scholarly journals TOWARDS DEEP LEARNING FOR ARCHITECTURE: A MONUMENT RECOGNITION MOBILE APP

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 551-556 ◽  
Author(s):  
V. Palma
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
State Of The Art ◽  
Mobile App ◽  
Information Modeling ◽  
Computational Power ◽  
Architectural Heritage ◽  
Research Stream ◽  
Heritage Collections

<p><strong>Abstract.</strong> In recent years, the diffusion of large image datasets and an unprecedented computational power have boosted the development of a class of artificial intelligence (AI) algorithms referred to as deep learning (DL). Among DL methods, convolutional neural networks (CNNs) have proven particularly effective in computer vision, finding applications in many disciplines. This paper introduces a project aimed at studying CNN techniques in the field of architectural heritage, a still to be developed research stream. The first steps and results in the development of a mobile app to recognize monuments are discussed. While AI is just beginning to interact with the built environment through mobile devices, heritage technologies have long been producing and exploring digital models and spatial archives. The interaction between DL algorithms and state-of-the-art information modeling is addressed, as an opportunity to both exploit heritage collections and optimize new object recognition techniques.</p>

scholarly journals S543 Validation of Novel Mobile App Using Artificial Intelligence to Capture and Objectively Characterize Stool Images in a Clinical Trial

2021 ◽  
Vol 116 (1) ◽  
pp. S247-S247
Author(s):  
Mark Pimentel ◽  
Ali Rezaie ◽  
Ruchi Mathur ◽  
Jiajing Wang ◽  
Asaf Kraus ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Mobile App

An Intelligent Mobile App to Detect Drowsy Driving with Artificial Intelligence

2021 ◽  
Author(s):  
Thomas Xiao ◽  
Yu Sun
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Normal Control ◽  
Qualitative Evaluation ◽  
Mobile App ◽  
Drowsy Driving ◽  
Evaluation Approach ◽  
Control Situation ◽  
Driving Condition

Drowsy driving is lethal- 793 died from accidents related to drowsy driving and 91000 accidents related to drowsy driving occurred [1]. However, drowsy driving and accidents related to drowsy driving are preventable. In this paper, we address the problem through an application that uses artificial intelligence to detect the eye openness of the user. The application can detect the eyes of the user via computer vision. Based on the user’s eye openness and frequencies, the sleepy driving condition can be inferred by this application. We applied our application to actual driving environments on the highway, both day and night, as well as within a normal control situation using a qualitative evaluation approach. The result shows that it is 88% effective during the day and 75% effective during nighttime. This result reveals effectiveness and accuracy of detection during daytime application under controlled testing, which is more flexible and efficient comparing to previous works. Effectiveness and accuracy for nighttime detection and detections with the presence of other distractions can be further improved.

scholarly journals Notes of Life: A platform for recording species observations driven by artificial intelligence

2019 ◽  
Vol 3 ◽  
Author(s):  
Congtian Lin ◽  
Jiangning Wang ◽  
Liqiang Ji
Keyword(s):  
Artificial Intelligence ◽  
Citizen Science ◽  
Higher Plants ◽  
Mobile App ◽  
Biodiversity Data ◽  
Science Data ◽  
Biodiversity Research ◽  
Large Numbers ◽  
In The Wild ◽  
Speed Up

Biodiversity research is stepping into a big data era with the rapid increase in the abundance of biodiversity data, especially the large number of species images. It has been a new trend and hot topic on how to utilize artificial intelligence to mine big biodiversity data to support wildlife observation and recognition. In this research, we integrate large numbers of species images, including higher plants, birds and insects, and use a state-of-the-art image deep learning technique to train species auto-recognition models. Currently, we get a model that can recognize more than 900 Chinese birds with top 1 accuracy 81% and top 5 accuracy 95% (top n accuracy means the probability that the correct answer presents in top n predicted results), and more models are coming soon. Based on these models, we developed a platform named Notes of Life (NOL, http://nol.especies.cn), which includes a website and a mobile application (app) for assisting biological scientists and citizen scientists to recognize and record wildlife. Users can upload their observation records and images of wildlife through our mobile app while they are investigating in the wild. The website is used for bulk data uploading and management. Species images can be classified by taxon-specific, plug-in recognition models that speed up the process of identification. There is an expert module in NOL where citizen scientists can work interactively with information provided by biological scientists, and post a species image identification request to experts when they cannot recognize the species by themselves or from models. The expert module is for improving the quality of citizen science data, and it is a supplement of the disadvantage of species auto-recognition models. Above all, NOL embraces the idea that scientific research supports citizen science and citizen science gives feedback to science, and of finding a sustainable way to collect increasingly more reliable data for biodiversity research.

scholarly journals An Artificial Intelligence–Based, Personalized Smartphone App to Improve Childhood Immunization Coverage and Timelines Among Children in Pakistan: Protocol for a Randomized Controlled Trial (Preprint)

2020 ◽  
Author(s):  
Abdul Momin Kazi ◽  
Saad Ahmed Qazi ◽  
Sadori Khawaja ◽  
Nazia Ahsan ◽  
Rao Moueed Ahmed ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Randomized Controlled Trial ◽  
Mobile Phone ◽  
Controlled Trial ◽  
Tertiary Care ◽  
Mobile App ◽  
University Hospital ◽  
Smartphone App ◽  
Childhood Immunization ◽  
Randomized Controlled

BACKGROUND The immunization uptake rates in Pakistan are much lower than desired. Major reasons include lack of awareness, parental forgetfulness regarding schedules, and misinformation regarding vaccines. In light of the COVID-19 pandemic and distancing measures, routine childhood immunization (RCI) coverage has been adversely affected, as caregivers avoid tertiary care hospitals or primary health centers. Innovative and cost-effective measures must be taken to understand and deal with the issue of low immunization rates. However, only a few smartphone-based interventions have been carried out in low- and middle-income countries (LMICs) to improve RCI. OBJECTIVE The primary objectives of this study are to evaluate whether a personalized mobile app can improve children’s on-time visits at 10 and 14 weeks of age for RCI as compared with standard care and to determine whether an artificial intelligence model can be incorporated into the app. Secondary objectives are to determine the perceptions and attitudes of caregivers regarding childhood vaccinations and to understand the factors that might influence the effect of a mobile phone–based app on vaccination improvement. METHODS A mixed methods randomized controlled trial was designed with intervention and control arms. The study will be conducted at the Aga Khan University Hospital vaccination center. Caregivers of newborns or infants visiting the center for their children’s 6-week vaccination will be recruited. The intervention arm will have access to a smartphone app with text, voice, video, and pictorial messages regarding RCI. This app will be developed based on the findings of the pretrial qualitative component of the study, in addition to <i>no-show</i> study findings, which will explore caregivers’ perceptions about RCI and a mobile phone–based app in improving RCI coverage. RESULTS Pretrial qualitative in-depth interviews were conducted in February 2020. Enrollment of study participants for the randomized controlled trial is in process. Study exit interviews will be conducted at the 14-week immunization visits, provided the caregivers visit the immunization facility at that time, or over the phone when the children are 18 weeks of age. CONCLUSIONS This study will generate useful insights into the feasibility, acceptability, and usability of an Android-based smartphone app for improving RCI in Pakistan and in LMICs. CLINICALTRIAL ClinicalTrials.gov NCT04449107; https://clinicaltrials.gov/ct2/show/NCT04449107 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/22996

scholarly journals Acceptability and Effectiveness of Artificial Intelligence Therapy for Anxiety and Depression (Youper): Longitudinal Observational Study

10.2196/26771 ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. e26771
Author(s):  
Ashish Mehta ◽  
Andrea Nicole Niles ◽  
Jose Hamilton Vargas ◽  
Thiago Marafon ◽  
Diego Dotta Couto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Emotion Regulation ◽  
Low Cost ◽  
Mobile App ◽  
Anxiety And Depression ◽  
Depression Symptoms ◽  
Symptom Reduction ◽  
Depression Symptom ◽  
Longitudinal Observational Study ◽  
Small Effect Size

Background Youper is a widely used, commercially available mobile app that uses artificial intelligence therapy for the treatment of anxiety and depression. Objective Our study examined the acceptability and effectiveness of Youper. Further, we tested the cumulative regulation hypothesis, which posits that cumulative emotion regulation successes with repeated intervention engagement will predict longer-term anxiety and depression symptom reduction. Methods We examined data from paying Youper users (N=4517) who allowed their data to be used for research. To characterize the acceptability of Youper, we asked users to rate the app on a 5-star scale and measured retention statistics for users’ first 4 weeks of subscription. To examine effectiveness, we examined longitudinal measures of anxiety and depression symptoms. To test the cumulative regulation hypothesis, we used the proportion of successful emotion regulation attempts to predict symptom reduction. Results Youper users rated the app highly (mean 4.36 stars, SD 0.84), and 42.66% (1927/4517) of users were retained by week 4. Symptoms decreased in the first 2 weeks of app use (anxiety: d=0.57; depression: d=0.46). Anxiety improvements were maintained in the subsequent 2 weeks, but depression symptoms increased slightly with a very small effect size (d=0.05). A higher proportion of successful emotion regulation attempts significantly predicted greater anxiety and depression symptom reduction. Conclusions Youper is a low-cost, completely self-guided treatment that is accessible to users who may not otherwise access mental health care. Our findings demonstrate the acceptability and effectiveness of Youper as a treatment for anxiety and depression symptoms and support continued study of Youper in a randomized clinical trial.

A Deep Learning Approach for Voice Disorder Detection for Smart Connected Living Environments

2022 ◽  
Vol 22 (1) ◽  
pp. 1-16
Author(s):  
Laura Verde ◽  
Nadia Brancati ◽  
Giuseppe De Pietro ◽  
Maria Frucci ◽  
Giovanna Sannino
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Mobile Devices ◽  
Voice Quality ◽  
Voice Disorders ◽  
Computation Time ◽  
Voice Disorder ◽  
Process Data ◽  
Mobile Health Application

Edge Analytics and Artificial Intelligence are important features of the current smart connected living community. In a society where people, homes, cities, and workplaces are simultaneously connected through various devices, primarily through mobile devices, a considerable amount of data is exchanged, and the processing and storage of these data are laborious and difficult tasks. Edge Analytics allows the collection and analysis of such data on mobile devices, such as smartphones and tablets, without involving any cloud-centred architecture that cannot guarantee real-time responsiveness. Meanwhile, Artificial Intelligence techniques can constitute a valid instrument to process data, limiting the computation time, and optimising decisional processes and predictions in several sectors, such as healthcare. Within this field, in this article, an approach able to evaluate the voice quality condition is proposed. A fully automatic algorithm, based on Deep Learning, classifies a voice as healthy or pathological by analysing spectrogram images extracted by means of the recording of vowel /a/, in compliance with the traditional medical protocol. A light Convolutional Neural Network is embedded in a mobile health application in order to provide an instrument capable of assessing voice disorders in a fast, easy, and portable way. Thus, a straightforward mobile device becomes a screening tool useful for the early diagnosis, monitoring, and treatment of voice disorders. The proposed approach has been tested on a broad set of voice samples, not limited to the most common voice diseases but including all the pathologies present in three different databases achieving F1-scores, over the testing set, equal to 80%, 90%, and 73%. Although the proposed network consists of a reduced number of layers, the results are very competitive compared to those of other “cutting edge” approaches constructed using more complex neural networks, and compared to the classic deep neural networks, for example, VGG-16 and ResNet-50.

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