المؤلفون: Gupta, Vibhuti¹, Maity, Sandip², Jhampati, Subhasis³

المصدر: Water and Energy International 64r(4):46-53. 2021

المؤلفون: Gupta, Vibhuti, Raj, Minakshi, Hoodin, Flora, Yahng, Lilian, Braun, Thomas, Choi, Sung Won

المصدر: JMIR Cancer, Vol 7, Iss 1, p e26509 (2021)

مصطلحات موضوعية: Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282

الوصف: BackgroundAs family caregivers of patients undergoing hematopoietic cell transplantation have multifaceted caregiving responsibilities (such as medical, household, financial) of long duration, they also have multiple physical, social, psychological, and informational needs. ObjectiveThis study explored the prevalence of electronic health record patient portal use by family caregivers for managing both their own and their hematopoietic cell transplantation care recipient’s health, as well as potential factors associated with portal use. MethodsAn electronic caregiver health survey, first developed via cognitive interviewing methods of hematopoietic cell transplantation caregivers, was distributed nationally (in the United States) by patient advocacy organizations to family caregivers of hematopoietic cell transplantation patients. It was used to assess self-reported caregiver demographics, caregiving characteristics, depression and anxiety with the Patient Health Questionnaire–4, coping with the Brief COPE, and caregiver portal use to manage care recipient’s and their own health. ResultsWe found that 77% of respondents (720/937) accessed electronic health record patient portals for their care recipients, themselves, or both. Multivariate models indicated use of care recipient electronic health record portals by caregivers was more likely with young, White, married, low-income caregivers caring for a parent, residing with the care recipient, and experiencing more caregiver depression. Caregiver use of their own electronic health record portal was more likely with young, White, high-income caregivers caring for a parent and experiencing chronic medical conditions of their own. Partially due to multicollinearity, anxiety and coping did not contribute independently to this model. ConclusionsFindings from the survey could open avenues for future research into caregiver use of technology for informational support or intervention, including wearables and mobile health. International Registered Report Identifier (IRRID)RR2-10.2196/4918

وصف الملف: electronic resource

العلاقة: https://cancer.jmir.org/2021/1/e26509Test; https://doaj.org/toc/2369-1999Test

الوصول الحر: https://doaj.org/article/f4fe393f4e334ae18783ee72119a5c89Test

المؤلفون: Gupta, Vibhuti, Hewett, Rattikorn

مصطلحات موضوعية: Computer Science - Distributed, Parallel, and Cluster Computing

الوصف: In todays digital era, data are everywhere from Internet of Things to health care or financial applications. This leads to potentially unbounded ever-growing Big data streams and it needs to be utilized effectively. Data normalization is an important preprocessing technique for data analytics. It helps prevent mismodeling and reduce the complexity inherent in the data especially for data integrated from multiple sources and contexts. Normalization of Big Data stream is challenging because of evolving inconsistencies, time and memory constraints, and non-availability of whole data beforehand. This paper proposes a distributed approach to adaptive normalization for Big data stream. Using sliding windows of fixed size, it provides a simple mechanism to adapt the statistics for normalizing changing data in each window. Implemented on Apache Storm, a distributed real-time stream data framework, our approach exploits distributed data processing for efficient normalization. Unlike other existing adaptive approaches that normalize data for a specific use (e.g., classification), ours does not. Moreover, our adaptive mechanism allows flexible controls, via user-specified thresholds, for normalization tradeoffs between time and precision. The paper illustrates our proposed approach along with a few other techniques and experiments on both synthesized and real-world data. The normalized data obtained from our proposed approach, on 160,000 instances of data stream, improves over the baseline by 89% with 0.0041 root-mean-square error compared with the actual data.

الوصول الحر: http://arxiv.org/abs/1910.07696Test

المؤلفون: Gupta, Vibhuti

مصطلحات موضوعية: Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning

الوصف: Automated detection of voice disorders with computational methods is a recent research area in the medical domain since it requires a rigorous endoscopy for the accurate diagnosis. Efficient screening methods are required for the diagnosis of voice disorders so as to provide timely medical facilities in minimal resources. Detecting Voice disorder using computational methods is a challenging problem since audio data is continuous due to which extracting relevant features and applying machine learning is hard and unreliable. This paper proposes a Long short term memory model (LSTM) to detect pathological voice disorders and evaluates its performance in a real 400 testing samples without any labels. Different feature extraction methods are used to provide the best set of features before applying LSTM model for classification. The paper describes the approach and experiments that show promising results with 22% sensitivity, 97% specificity and 56% unweighted average recall.

الوصول الحر: http://arxiv.org/abs/1812.01779Test

المؤلفون: Gupta, Vibhuti, Hewett, Rattikorn

المصدر: 2018, pp. 4554-4558

مصطلحات موضوعية: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Information Retrieval, Computer Science - Social and Information Networks

الوصف: Twitter is a popular social network platform where users can interact and post texts of up to 280 characters called tweets. Hashtags, hyperlinked words in tweets, have increasingly become crucial for tweet retrieval and search. Using hashtags for tweet topic classification is a challenging problem because of context dependent among words, slangs, abbreviation and emoticons in a short tweet along with evolving use of hashtags. Since Twitter generates millions of tweets daily, tweet analytics is a fundamental problem of Big data stream that often requires a real-time Distributed processing. This paper proposes a distributed online approach to tweet topic classification with hashtags. Being implemented on Apache Storm, a distributed real time framework, our approach incrementally identifies and updates a set of strong predictors in the Na\"ive Bayes model for classifying each incoming tweet instance. Preliminary experiments show promising results with up to 97% accuracy and 37% increase in throughput on eight processors.
Comment: IEEE International Conference on Big Data 2018

الوصول الحر: http://arxiv.org/abs/1812.01141Test

المؤلفون: Gupta, Vibhuti, Sinha, Nishit Kumar

المصدر: Vikalpa: The Journal for Decision Makers ; volume 48, issue 3, page 206-219 ; ISSN 0256-0909 2395-3799

مصطلحات موضوعية: General Business, Management and Accounting, General Decision Sciences

الوصف: ‘For after all, the best thing one can do when it is raining is, let it rain.’ ( Longfellow, 1906 ) Mental health conditions, including depression, anxiety, and stress, affect a sizable proportion of university students. Yet, little is known regarding the incidence of mental health issues among Indian university students. These mental health issues are generally caused by a range of factors, including academic demands, interpersonal connections ( Steptoe, 2007 ), future prospects, competitive exams, peer pressure, and professional considerations ( Beiter et al., 2015 ). One of the most important mental health problems impacting a large population across India and the world is depression, leading to physical diseases, suicidal thoughts, and suicide, among other negative outcomes ( Gururaj et al., 2016 ). According to the NCRB Report, 2021, the two age groups most susceptible to suicide were between 18 and 30 and 30 and 44. Suicide rates in both age categories were 34.5% and 31.7%, respectively. Family issues (3,233 victims), romantic relationships (1,495 victims), and illness (1,408 victims) were the three leading factors in suicides below 18 years of age, while the victims that were either students or unemployed made up 8.0% (13,089 victims) and 8.4% (13,714 victims) of all suicides, respectively. The objectives of this study were to understand and add to the body of knowledge on the role of mindfulness concerning depression, anxiety, and stress amongst university students; the role of mindfulness concerning avoidant behaviours amongst university students; and the mediating role of acceptance in the relationship between mindfulness and depression, anxiety, and stress amongst university students. The results of the study revealed that depression, experiential avoidance, and mindfulness are all strongly and negatively connected. The study discovered a strong correlation between experiential avoidance and depression, anxiety, and stress (psychological distress). Mindfulness had a considerable impact on the ...

الإتاحة: https://doi.org/10.1177/02560909231166498Test

المؤلفون: Taylor, Loni, Gupta, Vibhuti, Jung, Kwanghee

المصدر: Multimodal Technologies & Interaction; Apr2024, Vol. 8 Issue 4, p28, 24p

مصطلحات موضوعية: DATA-based decision making in education, ARTIFICIAL intelligence, DATA visualization, MACHINE learning, MICROSOFT Azure (Computing platform), INDIVIDUALIZED instruction

مصطلحات جغرافية: LOUISIANA, TEXAS

مستخلص: As data-driven models gain importance in driving decisions and processes, recently, it has become increasingly important to visualize the data with both speed and accuracy. A massive volume of data is presently generated in the educational sphere from various learning platforms, tools, and institutions. The visual analytics of educational big data has the capability to improve student learning, develop strategies for personalized learning, and improve faculty productivity. However, there are limited advancements in the education domain for data-driven decision making leveraging the recent advancements in the field of machine learning. Some of the recent tools such as Tableau, Power BI, Microsoft Azure suite, Sisense, etc., leverage artificial intelligence and machine learning techniques to visualize data and generate insights from them; however, their applicability in educational advances is limited. This paper focuses on leveraging machine learning and visualization techniques to demonstrate their utility through a practical implementation using K-12 state assessment data compiled from the institutional websites of the States of Texas and Louisiana. Effective modeling and predictive analytics are the focus of the sample use case presented in this research. Our approach demonstrates the applicability of web technology in conjunction with machine learning to provide a cost-effective and timely solution to visualize and analyze big educational data. Additionally, ad hoc visualization provides contextual analysis in areas of concern for education agencies (EAs). [ABSTRACT FROM AUTHOR]

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المؤلفون: Raj, Minakshi, Gupta, Vibhuti, Hoodin, Flora, Clingan, Caroline, Roslin, Chloe, Yahng, Lilian, Braun, Thomas, Choi, Sung Won

المساهمون: National Heart, Lung, and Blood Institute

المصدر: DIGITAL HEALTH ; volume 8, page 205520762211090 ; ISSN 2055-2076 2055-2076

مصطلحات موضوعية: Health Information Management, Computer Science Applications, Health Informatics, Health Policy

الوصف: Introduction Digital health technology-based interventions have the potential to support cancer caregivers in caregiving responsibilities and in managing their own health and well-being. The objective of this study was to examine the association between caregiving characteristics and different types of digital health technologies used in a national sample of caregivers of patients undergoing hematopoietic cell transplantation (HCT). Methods We conducted an online, cross-sectional survey of 948 HCT caregivers. Results Spousal caregivers comprised nearly one-third of respondents (27.1%) with a median age of 59 years (range: 18–80 years), compared with parents (32.9%: 38 years), adult children (28.9%: 38 years), and other (11.1%; e.g. friend, other family member: 36 years). Almost two-thirds (65.4%) of all respondents reported using an app for fitness or step counting and 41.3% reported using a smartwatch. However, spousal caregivers were the least likely group to use mobile apps (0.72; P < 0.005) or smartwatches (OR = 0.46; P < 0.005) compared with parent caregivers in models adjusted for demographics and coping style. Caregiving for six months or greater was associated with the use of fewer apps compared with caregiving for less than six months in adjusted models (OR = 0.80, P < 0.005). Caregivers of patients receiving an allogeneic transplant (i.e. non-self-donor) used more apps on average than caregivers of patients receiving an autologous transplant (i.e. self-donor) in adjusted models (OR = 1.36, P < 0.005). Conclusion Digital health technologies reflect promising avenues for supporting cancer caregivers. While digital technologies are becoming increasingly pervasive, older caregivers remain an underserved population. Future research should integrate older adult caregivers in the co-design and development activities of technology-driven caregiver support products.

الإتاحة: https://doi.org/10.1177/20552076221109071Test

المؤلفون: Nguyen, Vinh T, Jung, Kwanghee, Gupta, Vibhuti

المصدر: Visual Computing for Industry, Biomedicine, and Art ; volume 4, issue 1 ; ISSN 2524-4442

مصطلحات موضوعية: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Visual Arts and Performing Arts, Medicine (miscellaneous), Computer Science (miscellaneous), Software

الوصف: Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. To address common pitfalls in graphical representations, this paper focuses on identifying and understanding the root causes of misinformation in graphical representations. We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color, shape, size, and spatial orientation. Moreover, a text mining technique was applied to extract practical insights from common visualization pitfalls. Cochran’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color, shape, size, and spatial orientation. The findings showed that the pie chart is the most misused graphical representation, and size is the most critical issue. It was also observed that there were statistically significant differences in the proportion of errors among color, shape, size, and spatial orientation.

الإتاحة: https://doi.org/10.1186/s42492-021-00092-yTest

المؤلفون: Flora, Christopher, Tyler, Jonathan, Mayer, Caleb, Warner, David E., Khan, Shihan N., Gupta, Vibhuti, Lindstrom, Ryan, Mazzoli, Amanda, Rozwadowski, Michelle, Braun, Thomas M., Ghosh, Monalisa, Forger, Daniel B., Choi, Sung Won, Tewari, Muneesh

المصدر: Cancer Cell ; volume 39, issue 9, page 1167-1168 ; ISSN 1535-6108

مصطلحات موضوعية: Cancer Research, Oncology

الإتاحة: https://doi.org/10.1016/j.ccell.2021.07.019Test
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