scholarly journals Low-cost fitness and activity trackers for biometric authentication

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Saad Khan ◽  
Simon Parkinson ◽  
Na Liu ◽  
Liam Grant
Keyword(s):  
Low Cost ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Exploratory Research ◽  
Biometric Authentication ◽  
Activity Trackers ◽  
Rate Step ◽  
Data Source ◽  
Using Data ◽  
Rich Data

Abstract Fitness and activity tracking devices acquire, process and store rich behavioural data that are consumed by the end-user to learn health insights. This rich data source also enables a secondary use of being part of a biometric authentication system. However, there are many open research challenges with the use of data generated by fitness and activity trackers as a biometric source. In this article, the challenge of using data acquired from low-cost devices is tackled. This includes investigating how to best partition the data to deduce repeatable behavioural traits, while maximizing the uniqueness between participant datasets. In this exploratory research, 3 months’ worth of data (heart rate, step count and sleep) for five participants is acquired and utilized in its raw form from low-cost devices. It is established that dividing the data into 14-h segments is deemed the most suitable based on measuring coefficients of variance. Several supervised machine learning algorithms are then applied where the performance is evaluated by six metrics to demonstrate the potential of employing this data source in biometric-based security systems.

scholarly journals Whatsapp as Da’wah Sipakainge’ Media

2021 ◽  
Vol 3 (1) ◽  
pp. 58-72
Author(s):  
Mahmuddin
Keyword(s):  
Qualitative Research ◽  
Social Media ◽  
Low Cost ◽  
Analysis Techniques ◽  
Or Groups ◽  
Qualitative Descriptive ◽  
Data Source ◽  
Using Data ◽  
Sociological Approaches ◽  
Collection Methods

This study aims to determine the use of WhatsApp as a media of da'wah sipakainge' for UIN Alauddin Makassar students, by raising the problem, namely how to use WhatsApp for UIN Alauddin students and why WhatsApp is used as a media for da'wah sipakainge' by UIN Alauddin Makassar students. This study examines the problem of using WhatsApp as a medium for preaching sipakainge' for UIN Alauddin Makassar students. This type of research is qualitative research using media, da'wah and sociological approaches as well as communication approaches. The data source of this research is the students of UIN Alauddin Makassar using data collection methods through observation, questionnaires, interviews, and documentation. Data processing and analysis techniques are qualitative descriptive techniques. Based on the results of the research, it shows that students of UIN Alauddin Makassar have used WhatsApp as a media of da'wah for social media ', this was found in a number of questionnaire results and interviews with UIN Alauddin Makasar students, which stated that WhatsApp was a means of communication for students, WhatsApp was a media of da'wah, the use of WA in spreading sipakainge' da'wah, many students use WA in spreading sipakainge' da'wah to individuals or groups. Meanwhile, the reason WhatsApp is used as a medium for preaching sipakainge' by UIN Alauddin students is because of the low cost, because many students access it because it is easy to communicate in groups because it is very easy to find invitations of kindness as material for sipakainge’ da'wah because it is easy to send da'wah material to groups. The implication of this research is the need to utilize and direct students in using WhatsApp in the learning process and use it according to the actual function of creating WhatsApp media as well as possible.

scholarly journals Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 778
Author(s):  
Nitsa J. Herzog ◽  
George D. Magoulas
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Progressive Dementia ◽  
Machine Learning Classification

Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.

scholarly journals A nanoelectronics-blood-based diagnostic biomarker for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)

2019 ◽  
Vol 116 (21) ◽  
pp. 10250-10257 ◽  
Author(s):  
R. Esfandyarpour ◽  
A. Kashi ◽  
M. Nemat-Gorgani ◽  
J. Wilhelmy ◽  
R. W. Davis
Keyword(s):  
Chronic Fatigue Syndrome ◽  
Point Of Care ◽  
Low Cost ◽  
Chronic Fatigue ◽  
Myalgic Encephalomyelitis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Fatigue Syndrome ◽  
Impedance Modulation ◽  
Ultrasensitive Assay

There is not currently a well-established, if any, biological test to diagnose myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The molecular aberrations observed in numerous studies of ME/CFS blood cells offer the opportunity to develop a diagnostic assay from blood samples. Here we developed a nanoelectronics assay designed as an ultrasensitive assay capable of directly measuring biomolecular interactions in real time, at low cost, and in a multiplex format. To pursue the goal of developing a reliable biomarker for ME/CFS and to demonstrate the utility of our platform for point-of-care diagnostics, we validated the array by testing patients with moderate to severe ME/CFS patients and healthy controls. The ME/CFS samples’ response to the hyperosmotic stressor observed as a unique characteristic of the impedance pattern and dramatically different from the response observed among the control samples. We believe the observed robust impedance modulation difference of the samples in response to hyperosmotic stress can potentially provide us with a unique indicator of ME/CFS. Moreover, using supervised machine learning algorithms, we developed a classifier for ME/CFS patients capable of identifying new patients, required for a robust diagnostic tool.

scholarly journals Exploratory study: the COVID-19 pandemic and community-based animal organisations and households in the USA

2021 ◽  
Vol 10.47389/36 (36.3) ◽  
pp. 85-91
Author(s):  
Sarah DeYoung ◽  
Ashley Farmer
Keyword(s):  
Online Survey ◽  
Low Cost ◽  
Companion Animals ◽  
Community Services ◽  
Exploratory Research ◽  
Community Based ◽  
Household Level ◽  
Emergency Managers ◽  
The Usa ◽  
Using Data

The COVID-19 pandemic has changed many aspects of human systems. Gaps in community services for people with companion animals can prevent people from seeking care during a pandemic or create other issues. This paper describes exploratory research to identify some key challenges and successes for animal services providers and for households with companion animals. Using data from 19 USA states were gathered using an online survey and respondents were from 13 animal services organisations and 90 households. Themes were identified based on organisational-level challenges or successes, as well as themes at the household level. These findings may be useful for emergency managers and planners who design outreach and support services for people with companion animals, for example, planning for low-cost animal boarding services for people hospitalised or unable to care for their animal.

scholarly journals The Uses of the "Temporal-Fares-Offered Curve" in Air Transportation

2010 ◽  
Author(s):  
Kenneth Button ◽  
Henry Vega
Keyword(s):  
Low Cost ◽  
Empirical Work ◽  
Air Transportation ◽  
Critical Assessment ◽  
Pricing Strategies ◽  
Price Leadership ◽  
Data Source ◽  
Rich Data ◽  
Use Of Internet

The increasing use of Internet booking facilities provides analysts with a rich data source of the profile of airline fares offered for a particular service as the time of departure approaches – "temporal-fares-offered curves." This paper offers a critical assessment of this form of analysis. It also reviews the empirical work that has been done using this type of data and synthesizes the information and insights that it can provide on the operations of an airline market. The subjects covered range from pricing strategies of low-cost and legacy carriers under different degrees of competition, to the extent to which there is price leadership in markets, and to ways in which airlines determine fares-offered when their schedules mean that their own services effectively compete with each other.

scholarly journals The Classification of Customers’ Sentiment using Data Mining Approaches

2019 ◽  
Vol IV (IV) ◽  
pp. 146-156
Author(s):  
Dost Muhammad Khan ◽  
Tariq Aziz Rao ◽  
Faisal Shahzad
Keyword(s):  
Data Mining ◽  
Service Providers ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Supervised Machine Learning ◽  
Market Trends ◽  
Using Data

Data mining is a procedure of extracting the requisite information from unprocessed records by using certain methodologies and techniques. Data having sentiments of customers is of utmost importance for managers and decision-makers who intend to monitor the progress, to maintain the quality of their products or services and to observe the latest market trends for business support. Billions of customers are using micro-blogging websites and social media for sharing their opinions about different topics on daily basis. Therefore, it has become a source of acquiring information but to identify a particular feature of a product is still an issue as the information retrieves from varied sources. We proposed a framework for data acquisition, preprocessing, feature extraction and used three supervised machine-learning algorithms for classification of customers’ sentiments. The proposed framework also tested to evaluate the system’s performance. Our proposed methodology will be helpful for researchers, service providers, and decisionmakers.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

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