scholarly journals Dementia Prediction in Older People through Topic-cued Spontaneous Conversation

2021 ◽  
Author(s):  
Tomasz Maciej Rutkowski ◽  
Masato S Abe ◽  
Seiki Tokunaga ◽  
Mihoko Otake-Matsuura
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Well Being ◽  
Coefficient Of Determination ◽  
Elderly Subjects ◽  
Prediction Errors ◽  
Social Good ◽  
Study Results ◽  
Natural Conversation ◽  
Dementia Onset

An increase in dementia cases is producing significant medical and economic pressure in many communities. This growing problem calls for the application of AI-based technologies to support early diagnostics, and for subsequent non-pharmacological cognitive interventions and mental well-being monitoring. We present a practical application of a machine learning (ML) model in the domain known as 'AI for social good'. In particular, we focus on early dementia onset prediction from speech patterns in natural conversation situations. This paper explains our model and study results of conversational speech pattern-based prognostication of mild dementia onset indicated by predictive Mini-Mental State Exam (MMSE) scores. Experiments with elderly subjects are conducted in natural conversation situations, with four members in each study group. We analyze the resulting four-party conversation speech transcripts within a natural language processing (NLP) deep learning framework to obtain conversation embedding. With a fully connected deep learning model, we use the conversation topic changing distances for subsequent MMSE score prediction. This pilot study is conducted with Japanese elderly subjects within a healthy group. The best median MMSE prediction errors are at the level of 0.167, with a median coefficient of determination equal to 0.330 and a mean absolute error of 0.909. The results presented are easily reproducible for other languages by swapping the language model in the proposed deep-learning conversation embedding approach.

Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation (Preprint)

2021 ◽  
Author(s):  
Hamed Jelodar
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Language Processing ◽  
Well Being ◽  
Emotional Reaction ◽  
Emotion Classification ◽  
Media Messages ◽  
Intelligence Models ◽  
Artificial Intelligence Models

BACKGROUND Given the limitations of medical diagnosis of early emotional change signs during the COVID-19 quarantine period, artificial intelligence models provide effective mechanisms in uncovering early signs, symptoms and escalating trend. OBJECTIVE The main purpose of this project is to demonstrate the effectiveness of Artificial Intelligence, and in particular Natural Language Processing and Machine Learning in detecting and analyzing emotions from tweets talking about COVID-19 social confinement. METHODS We developed a systematic framework that can be directly applied to COVID-19 related mood discovery, using eight types of emotional reaction and designing a deep learning model to uncover emotions based on the first wave of the pandemic public health restriction of mandatory social segregation. We argue that the framework can discover semantic trends of COVID-19 tweets during the first wave of the pandemic to predict new concerns that may be associated with furthering into the new waves of COVID-19 quarantine orders and other related public health regulations. RESULTS Our findings revealed Stay-At-Home restrictions result in people expressing on twitter both negative and positive based on emotional and semantics aspects. Moreover, the statistical results of the emotion classification is show that our framework based on CNN deep learning has predicted the emotion levels or target labels with more F1-socore than the LSTM model, which are 0.95% and 0.93%, respectively. However, these results have potential to impact public health policy decisions through monitoring trends of emotional feelings of those who are quarantined. CONCLUSIONS The research shows that the framework is effective in capturing the emotions and semantics trends in social media messages during the pandemic. Moreover, the framework can be applied to uncover reactions to similar public health policies that affect people’s well-being.

scholarly journals SEED: Symptom Extraction from English Social Media Posts using Deep Learning and Transfer Learning

2021 ◽  
Author(s):  
Arjun Magge ◽  
Karen O’Connor ◽  
Matthew Scotch ◽  
Gonzalez-Hernandez Graciela
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Language Processing ◽  
Viral Infections ◽  
Drug Effects ◽  
Self Report ◽  
Training Data ◽  
Social Media Mining ◽  
Study Results ◽  
Media Mining

AbstractThe increase of social media usage across the globe has fueled efforts in public health research for mining valuable information such as medication use, adverse drug effects and reports of viral infections that directly and indirectly affect human health. Despite its significance, such information can be incredibly rare on social media. Mining such non-traditional sources for disease monitoring requires natural language processing techniques for extracting symptom mentions and normalizing them to standard terminologies for interpretability. In this work, we present the first version of a social media mining tool called SEED that detects symptom and disease mentions from social media posts such as Twitter and DailyStrength and further normalizes them into the UMLS terminology. Using multi-corpus training and deep learning models, the tool achieves an overall F1 score of 0.85 for extracting mentions of symptoms on a health forum dataset and an F1 score of 0.72 on a balanced Twitter dataset significantly improving over previously systems on the datasets. We apply the tool on recently collected Twitter posts that self-report COVID19 symptoms to observe if the SEED system can extract novel diseases and symptoms that were absent in the training data. By doing so, we describe the advantages and shortcomings of the tool and suggest techniques to overcome the limitations. The study results also draw attention to the potential of multi-corpus training for performance improvements and the need for continual training on newly obtained data for consistent performance amidst the ever-changing nature of the social media vocabulary.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Work Life Balance and Job Satisfaction - A Structural Equation Modeling

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 156-162
Author(s):  
Dr. D. Shoba ◽  
Dr. G. Suganthi
Keyword(s):  
Job Satisfaction ◽  
Structural Equation ◽  
Well Being ◽  
Work Life ◽  
Work Life Balance ◽  
Equation Modeling ◽  
Subjective Well Being ◽  
Life Balance ◽  
Study Results

Work-Life balance has its importance from ancient days and the concept is very old, from the day the world has been created. There was a drastic change that has occurred in the market of teachers and their personal profiles. There are tremendous changes in various families which have bartered from the ‘breadwinner’ role of traditional men to single parent families and dual earning couples. This study furnishes an insight into work life balance and job satisfaction of teachers working in School of Villupuram District. The sample comprises of 75 school teachers from Government and private schools in Villupuram District. The Study results that there is increasing mediating evidence in Work-life balance as well as Job satisfaction of teachers are not affected by the type of school in which they are working. Job satisfaction or Pleasure of life will be affected as a whole by Work life balance of an individual which is the main which can be calculated by construct of subjective well being.

URGENSI AUDIT JUDGEMENT PADA POST CLEARANCE AUDIT

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Masruri Muchtar ◽  
Prasetya Utama
Keyword(s):  
Self Efficacy ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Experience Level ◽  
Level Of Education ◽  
Future Studies ◽  
Ordinary Least Square ◽  
Audit Judgment ◽  
Depth Interview ◽  
Study Results

ABSTRACT:The auditor should have eminence audit judgment to support their assignment This research aims to provide empirical evidence that self-efficacy, experience, level of education, and skepticism have an impact on audit judgment. The population are auditors who had carried out post-clearance audit assignments. This research uses a quantitative approach by testing the theories and hypotheses that have been prepared. Ordinary least square (OLS) linear regression as an analytical model is used in this study. Results show that experience and education level have no impact on audit judgment, whereas self-efficacy and skepticism have a positive and significant impact on audit judgment. Efforts to improve self-efficacy and auditor skepticism are urgently needed. The coefficient of determination describes the variation of variables of self-efficacy, experience, level of education, and skepticism able to explain the variation of audit judgment variables by 51%. The remaining 49% is explained by other variables not involved in this study. Future studies may enhance with other variables and employ in-depth interview methods.Keywords: audit judgment, experience, level of education, post-clearance audit, self-efficacy, skepticism, post-clearance audit ABSTRAK:Auditor seyogyanya memiliki kemampuan audit judgment yang berkualitas guna mendukung penugasannya. Tujuan penelitian adalah memberikan bukti empiris bahwa efikasi diri, pengalaman, tingkat pendidikan, dan skeptisisme memiliki pengaruh terhadap audit judgement. Populasi dalam penelitian ini adalah auditor Direktorat Jenderal Bea dan Cukai (DJBC) yang pernah melakukan post clearance audit. Ini merupakan pendekatan kuantitatif yang menguji teori serta hipotesis yang telah disusun. Riset ini menggunakan regresi linear ordinary least square (OLS) sebagai model analisis. Hasil studi memperlihatkan pengalaman dan tingkat pendidikan tidak berpengaruh pada audit judgement, namun efikasi diri dan skeptisisme berpengaruh signifikan pada audit judgement. Implikasinya DJBC perlu memberikan perhatian khusus terhadap berbagai upaya dalam peningkatan efikasi diri dan skeptisisme auditor. Tulisan ini adalah pengembangan beberapa penelitian sebelumnya namun dalam konteks pengujian untuk jenis audit ketaatan. Nilai koefisien determinasi menggambarkan variasi variabel efikasi diri, pengalaman, tingkat pendidikan, dan skeptisisme dapat menjelaskan variasi variabel audit judgement sebesar 51%. Sisanya sebesar 49% dijelaskan oleh variabel lainnya yang tidak diujikan dalam tulisan ini. Dengan adanya keterbatasan waktu pada penelitian ini diharapkan mendorong penelitian berikutnya untuk dapat menyertakan beberapa variabel lain yang relevan dan melengkapinya dengan metode in-depth interview.Kata Kunci: bea dan cukai, efikasi diri, pengalaman, skeptisisme, tingkat pendidikan

Acculturative stress and life satisfaction of Chinese rural-to-urban migrants

2020 ◽  
Vol 48 (11) ◽  
pp. 1-11
Author(s):  
Dekuo Liang ◽  
Lei Wang ◽  
Liying Xia ◽  
Dawei Xu
Keyword(s):  
Social Support ◽  
Life Satisfaction ◽  
Acculturative Stress ◽  
Perceived Social Support ◽  
Well Being ◽  
Self Esteem ◽  
Construction Sites ◽  
Urban Migrants ◽  
Study Results ◽  
The Relationship

Little is known regarding the life satisfaction of rural-to-urban migrants in China. In this study we assessed whether self-esteem and perceived social support mediated the association between rural-to-urban migrants' acculturative stress and life satisfaction. We use convenience sampling to recruit 712 migrants who were employed at construction sites in Nanjing for the study. Results reveal that acculturative stress was negatively related to self-esteem, perceived social support, and life satisfaction; self-esteem was positively associated with perceived social support and life satisfaction; and perceived social support was a significant and positive predictor of life satisfaction. In addition, we found that self-esteem and perceived social support partially mediated the relationship between acculturative stress and life satisfaction. Our findings provide a better understanding of life satisfaction over the course of migration, and add to knowledge of psychological well-being and mental health among rural-to-urban migrants in China.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

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