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IAMHAPPY: Towards an IoT knowledge-based cross-domain well-being recommendation system for everyday happiness

Smart Health 2020年 15卷

作者： Gyrard, Amelie Sheth, Amit Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) Wright State University OH United States

Nowadays, healthy lifestyle, fitness, and diet habits have become central applications in our daily life. Positive psychology such as well-being and happiness is the ultimate dream of everyday people's feelings (even without being aware of it). Wearable devices are being increasingly employed to support well-being and fitness. Those devices produce physiological signals that are analyzed by machines to understand emotions and physical state. The Internet of Things (IoT) technology connects (wearable) devices to the Internet to easily access and process data, even using Web technologies (aka Web of Things). We design IAMHAPPY, an innovative IoT-based well-being recommendation system to encourage every day people's happiness. The system helps people deal with day-to-day discomforts (e.g., minor symptoms such as headache, fever) by using home remedies and related alternative medicines (e.g., naturopathy, aromatherapy), activities to reduce stress, etc. To achieve this system, we build a web-based knowledge repository for emotion with a focus on happiness and well-being. The knowledge repository helps analyze data produced by IoT devices to understand users' emotions and health. The semantics-based knowledge repository is integrated with a rule-based engine to suggest recommendations to achieve everyday people's happiness. The naturopathy application scenario supports the recommendation system. © 2019 Elsevier Inc.

关键词： Affective science Emotion Happiness Inference engine Internet of things (IoT) knowledge directory service Ontology validation Recommender systems (RS) Reusability Reusable knowledge Rule-based reasoning Semantic ontology interoperability Semantic web of things (SWoT) Semantic web technologies Well-being Wellness

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Developing Machine Learning Model for Disambiguate Pattern Recognition on Social Media

Developing Machine Learning Model for Disambiguate Pattern R...

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International Conference on Computation, Automation and knowledge Management (ICCAKM)

作者： Farahnaz Golrooy Motlagh Huthaifa A. Al Issa Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) Wright State University Dayton USA Al-Huson University College Al-Balqa Applied University Jordan

ISBN: (数字)9781728106663

ISBN: (纸本)9781728106670

Social media data have been shown to be a useful resource for the recognition of novel drug use practices and trends. In a random sample collected using Twitter Application Programming Interface (API), “dabs” related tweets produced high volume data than other keywords related to marijuana concentrate in all the states that have legalized for recreational use or for medical use. The key objectives of this research are: 1-Describing a development model of a Machine Learning (ML) classifier to identify “dabs“ that are related to marijuana concentrates and evaluate its performance. 2-Examining dabs-related data between March 2016 and June 2017 after application of the ML classifier model. 3-Analyzing the trends on the number of relevant tweets, number of tweets with retweets, and unique users.

关键词： application program interfaces drugs learning (artificial intelligence) medical information systems pattern classification random processes social networking (online) Application programming interfaces medical information systems Pattern recognition Social Media Stochastic Processes Relining Pharmaceutical Preparations TWITTER development model Artificial Intelligence Machine Learning

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To develop Lean Systems in Electronic Automotive Parts Manufacturing Industry: A System Dynamics Modelling Approach

To develop Lean Systems in Electronic Automotive Parts Manuf...

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2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019

作者： Shetty, Dasharathraj K Abakari, Gautami Rodrigues, Dr. Lewlyn L R Oommen Mathew, Asish Motlagh, Farahnaz Golrooy Department of Humanities and Management Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Ohio Center of Excellence in Knowledge Enabled Computing Dept. of CSE Wright State University United States

ISBN: (纸本)9781538680100

The Electronic Automotive parts manufacturing company produces different types of components that are stored in different buffers. Kanban method is the most standard control methods in practice. The alternative method for this would be Heijunka approach or the production levelling approach which focuses on neutralizing the demand variation. A dynamic systems approach is proposed in this paper to investigate the challenges of implementing production levelling. A model is developed using system dynamics to stabilize the production units. The results obtained in this paper illustrate the concept of Heijunka which helps reduce the fluctuation in the upstream supply chain. Converting of uneven customer demand into predictable manufacturing process leading to better results in the buffers and maintaining minimal inventory is a challenge to be tackled effectively, which has been considered in this research to be the primary objective. The research has resulted in the backlog reduction strategy at the two strategic locations, smoothening of shipment rate, meeting of the desired production rate, better anticipating of order rate, minimization of inventory, better order fulfilment rate, and faster production adjustment. © 2019 IEEE.

关键词： Lean production

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Industry-Institute Technology Management Practices: A Case Study

Industry-Institute Technology Management Practices: A Case S...

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2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019

作者： Shenoy, Soujanya S Shetty, Dasharathraj K Rodrigues, Lewlyn L R Maddodi, Balakrishna S Motlagh, Farahnaz Golrooy Department of Humanities and Management Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Civil Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Ohio Center of Excellence in Knowledge Enabled Computing Dept. of CSE Wright State University United States

ISBN: (纸本)9781538680100

This paper investigates the institute-industry technology management (TM) practices based on the five key dimensions of Gregory's Process-Based Model. The research methodology involves a case study approach, involving a questionnaire survey of 196 faculty members from engineering institutes and 31 professionals from leading printing industries. While Acquisition of knowledge has been the top priority dimension in institutes, selection of knowledge is the top priority dimension in industries. Selection of knowledge is the second leading dimension in institute whereas, protection of knowledge is the second priority dimension in the industries. Identification of knowledge is the least prioritized dimension both in institutes and industries. This revelation has led to the drawing of implications to the managers of both the sectors to enhance the TM practices. © 2019 IEEE.

关键词： Surveys

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Development of an Integrated knowledge and Technology Management Model for the Higher Education

Development of an Integrated Knowledge and Technology Manage...

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2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019

作者： Thimmappa, B.H.S. Motlagh, Farahnaz Golrooy Shetty, Dasharathraj K Rajendra, B. Pai, Asha S Rodrigues, Lewlyn L R Department of Chemistry Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Ohio Center of Excellence in Knowledge Enabled Computing Dept. of CSE Wright State University United States Department of Humanities and Management Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Mechanical and Manufacturing Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Print and Media Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9781538680100

The present higher educational institutions are vying for excellence and strive to meet the expectations of individuals, organizations and the social institutions in their external environments. knowledge Management (KM) and Total Quality Management are the most common of the tools which these institutions have adopted based on the success stories in the industrial organizations. Considering these dual paradigms in higher education, this paper focuses on the integrated approach that makes an attempt to combine KM into TQM processes/practices. The paper is oriented towards the perspectives on organizational excellence that can be achieved by incorporating KM concepts into the TQM processes, which forms a cycle of continuous improvement and development. The article identifies the key parameters with suppliers and customers as the external environment acting on input and output of the system linked to organizational excellence. The perspectives on the integrated approach in higher education are also discussed addressing the demand and supply side of higher education. © 2019 IEEE.

关键词： knowledge management

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PhD Forum: Multimodal IoT and EMR Based Smart Health Application for Asthma Management in Children

PhD Forum: Multimodal IoT and EMR Based Smart Health Applica...

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International Conference on Smart computing (SMARTCOMP)

作者： Utkarshani Jaimini Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) Wright State University Dayton OH USA

ISBN: (纸本)9781509065189

According to a study done in 2014 by National Health Interview Survey around 6.3 million children in United States suffer from asthma [1]. Asthma remains one of the leading reasons for pediatric admissions to children's hospitals, and has a prevalence rate of approximately 10% in children and it leads to missed days from school and other societal costs. This occurs despite improved medications to control asthma symptoms. Asthma management is challenging as it involves understanding asthma causes and avoiding asthma triggers that are both multi- factorial and individualistic in nature. It is almost impossible for doctors to constantly monitor each patient's health and environmental triggers. According to a recent article, the IoT device market in health-care will increase to a worth of $117 billion by the year 2020 [2]. The monitoring segment of IoT devices have predicted to increase $15 billion in 2017 [5]. The sales of smart watches, fitness and health trackers, are expected to account for more than 70% of all wearables sale worldwide in 2016 [6]. According to IBM, the volume of health-care data has reached to 150 exabytes in 2017 [7]. The data generated from these consumer graded devices is increasing day by day. This data collection has exacerbated the problem of understanding the data and making sense of it. We can use these low-cost sensors and consumer graded devices for continuous monitoring and management of asthma patients. We developed kHealth 1 , a framework for continuous monitoring of the patient's personal, public and population-based health signals and send alerts to the patient when a condition deserves patient's or clinician's attention. This can assist the clinician in determining the triggers and deciding the future course of action for prevention and treatment of the disease. More importantly, it can also help a patient to better take control of his/her health management by taking more timely actions(e.g., in case of asthma, using an inhaler in a

关键词： Pediatrics Air quality Temperature measurement Medical services Monitoring Sensors Humidity

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The SSN Ontology of the W3C Semantic Sensor Network Incubator Group

SSRN

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SSRN 2018年

作者： Compton, Michael Barnaghi, Payam Bermudez, Luis García-Castro, Raúl Corcho, Oscar Cox, Simon Graybeal, John Hauswirth, Manfred Henson, Cory Herzog, Arthur Huang, Vincent Janowicz, Krzysztof Kelsey, W. David Le Phuoc, Danh Lefort, Laurent Leggieri, Myriam Neuhaus, Holger Nikolov, Andriy Page, Kevin Passant, Alexandre Sheth, Amit Taylor, Kerry CSIRO ICT Centre Australia University of Surrey United Kingdom Open Geospatial Consortium United States Universidad Politécnica de Madrid Spain CSIRO Earth Science and Resource Engineering Australia Digital Enterprise Research Institute DERI The National University of Ireland Galway Ireland Wright State University United States Fraunhofer Gesellschaft Germany Ericsson The Open University United Kingdom University of Southampton United Kingdom Ontology Engineering Group Marine Metadata Interoperability Department of Geography Tasmanian ICT Centre Ohio Center of Excellence in Knowledge-Enabled Computing Kno.e.sis

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL~2 ontology to describe sensors and observations - the SSN ontology. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects. © 2018, The Authors. All rights reserved.

关键词： Ontology

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“When 'bad' is 'good'”: Identifying personal communication and sentiment in drug-related tweets

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JMIR Public Health and Surveillance 2016年第2期2卷 e162页

作者： Daniulaityte, Raminta Chen, Lu Lamy, Francois R. Carlson, Robert G. Thirunarayan, Krishnaprasad Sheth, Amit Center for Interventions Treatment and Addictions Research Department of Population and Public Health Sciences Boonshoft School of Medicine Wright State University Kettering OH United States Ohio Center of Excellence in Knowledge-enabled Computing Department of Computer Science and Engineering Wright State University Dayton OH United States

Background: To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. Objectives: The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid-related tweets. Methods: Tweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25% (1000/4000) were used to build source classifiers and 75% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-s

关键词： Cannabis EDrugTrends Machine learning Sentiment analysis Social media Synthetic cannabinoids Twitter

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Semantic Gateway as a Service Architecture for IoT Interoperability

Semantic Gateway as a Service Architecture for IoT Interoper...

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IEEE International Conference on Mobile Services (MS)

作者： Pratikkumar Desai Amit Sheth Pramod Anantharam SeerLabs Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) Wright State University Dayton OH

The Internet of Things (IoT) is set to occupy a substantial component of future Internet. The IoT connects sensors and devices that record physical observations to applications and services of the Internet[1]. As a successor to technologies such as RFID and Wireless Sensor Networks (WSN), the IoT has stumbled into vertical silos of proprietary systems, providing little or no interoperability with similar systems. As the IoT represents future state of the Internet, an intelligent and scalable architecture is required to provide connectivity between these silos, enabling discovery of physical sensors and interpretation of messages between the things. This paper proposes a gateway and Semantic Web enabled IoT architecture to provide interoperability between systems, which utilizes established communication and data standards. The Semantic Gateway as Service (SGS) allows translation between messaging protocols such as XMPP, CoAP and MQTT via a multi-protocol proxy architecture. Utilization of broadly accepted specifications such as W3Cs Semantic Sensor Network (SSN) ontology for semantic annotations of sensor data provide semantic interoperability between messages and support semantic reasoning to obtain higher-level actionable knowledge from low-level sensor data.

关键词： Sensors Semantics Protocols Logic gates Interoperability Standards Ontologies

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knowledge-Driven Personalized Contextual mHealth Service for Asthma Management in Children

Knowledge-Driven Personalized Contextual mHealth Service for...

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IEEE International Conference on Mobile Services (MS)

作者： Pramod Anantharam Tanvi Banerjee Amit Sheth Krishnaprasad Thirunarayan Surendra Marupudi Vaikunth Sridharan Shalini G. Forbis Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) Wright State University Dayton OH Dept of Pediatrics Boonshoft School of Medicine Dayton OH

Wide adoption of smartphones and availability of low-cost sensors has resulted in seamless and continuous monitoring of physiology, environment, and public health notifications. However, personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context. Contextual processing of patient data along with personalized medical knowledge can lead to actionable information for better and timely decisions. We present a system called kHealth capable of aggregating multisensory and multimodal data from sensors (passive sensing) and answers to questionnaire (active sensing) from patients with asthma. We present our preliminary data analysis comprising data collected from real patients highlighting the challenges in deploying such an application. The results show strong promise to derive actionable information using a combination of physiological indicators from active and passive sensors that can help doctors determine more precisely the cause, severity, and control level of asthma. Information synthesized from kHealth can be used to alert patients and caregivers for seeking timely clinical assistance to better manage asthma and improve their quality of life.

关键词： Sensors Pediatrics Medical diagnostic imaging Monitoring Mobile handsets Lungs

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