RETRACTED ARTICLE: Deep learning model for home automation and energy reduction in a smart home environment platform

Popa, Dan O.; Pop, Florin; Şerbănescu, Cristina; Castiglione, Aniello

doi:10.1007/s00521-018-3724-6

Cited by 63 publications

(32 citation statements)

References 33 publications

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“…The nodes are able to perform complex, non-linear, computation on a set of input features, and give a suggested solution as an output. This new structure has been used to resolve many complex computer science problems such as image and speech recognition, with better accuracy compared to previous approaches of ML [19,20]. Convolutional neural networks (CNN) are a type of feed-forward neural network, which dates back to the 1980s.…”

Section: Classification Method: the 1d Convolutional Neural Networkmentioning

confidence: 99%

Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events

Abo-Tabik

Costen

Darby

et al. 2020

Sensors

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Nicotine consumption is considered a major health problem, where many of those who wish to quit smoking relapse. The problem is that overtime smoking as behaviour is changing into a habit, in which it is connected to internal (e.g., nicotine level, craving) and external (action, time, location) triggers. Smoking cessation apps have proved their efficiency to support smoking who wish to quit smoking. However, still, these applications suffer from several drawbacks, where they are highly relying on the user to initiate the intervention by submitting the factor the causes the urge to smoke. This research describes the creation of a combined Control Theory and deep learning model that can learn the smoker’s daily routine and predict smoking events. The model’s structure combines a Control Theory model of smoking with a 1D-CNN classifier to adapt to individual differences between smokers and predict smoking events based on motion and geolocation values collected using a mobile device. Data were collected from 5 participants in the UK, and analysed and tested on 3 different machine learning model (SVM, Decision tree, and 1D-CNN), 1D-CNN has proved it’s efficiency over the three methods with average overall accuracy 86.6%. The average MSE of forecasting the nicotine level was (0.04) in the weekdays, and (0.03) in the weekends. The model has proved its ability to predict the smoking event accurately when the participant is well engaged with the app.

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Section: Classification Method: the 1d Convolutional Neural Networkmentioning

confidence: 99%

Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events

Abo-Tabik

Costen

Darby

et al. 2020

Sensors

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“…At this moment, the presence of IoT in our lives is obvious through the smart home applications based on IoT, and the adoption trend is exponential [47,48].…”

Section: Discussionmentioning

confidence: 99%

On the rewards of self-adaptive IoT honeypots

et al. 2019

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In an era of fully digitally interconnected people and machines, IoT devices become a real target for attackers. Recent incidents such as the well-known Mirai botnet, have shown that the risks incurred are huge and therefore a risk assessment is mandatory. In this paper we present a novel approach on collecting relevant data about IoT attacks. We detail a SSH/Telnet honeypot system that leverages reinforcement learning algorithms in order to interact with the attackers, and we present the results obtained in view of defining optimal reward functions to be used. One of the key issues regarding the performance of such algorithms is the direct dependence on the reward functions used. The main outcome of our study is a full implementation of an IoT honeypot system that leverages Apprenticeship Learning using Inverse Reinforcement Learning, in order to generate best suited reward functions.

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“…In one study, “Participants reported using a wide variety of technology items, particularly in their homes. Positive attitudes (i.e., likes) outnumbered negative attitudes (i.e., dislikes), suggesting that older adults perceive the benefits of technology use to outweigh the costs of such use” 30 . In another study, data were collected from 254 subjects and concluded that “self‐capability, and satisfaction are positively related to the elderly intention in using smart‐homes, whereas there is a negative association between affordability, security/privacy.” 19 A majority had positive intentions of using the technology, decreasing as they get older, but often cited usability did not meet their expectations, which supports the need of Geriatric Engineers to work with Geriatric Practitioners and older adults to design tailored systems to meet the needs of older adults.…”

Section: Datamentioning

confidence: 99%

Geriatric Engineering: An Engineering Systems Design Approach to Human Aging

Lubliner

2020

J American Geriatrics Society

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This article explores the development of an interdisciplinary Geriatric Engineering 5‐year Bachelor of Science degree program. The process evolved over 5 years and began with exploring the need, precedents, and data acquisition from target individuals 60 to 92 years of age. The economics of aging necessitates training engineers to design and implement systems that drastically reduce costs. The literature search confirmed the need for medical and engineering collaboration. The precedents for a medical‐engineering curriculum were found to exist in several graduate MD/MS Engineering programs. Finally, data were gathered from several hundred over 60‐year olds, via a research questionnaire and interviews that supported that technology assistance to promote independent living was desired by a significant portion of older individuals. With confirmatory data in hand, geriatric engineering courses were introduced in 2017 at an engineering college. Feedback of more than 100 students who completed these courses was positive. An Advisory Board, composed of medical companies, hospitals, and target population individuals, supported a Geriatrics Engineering minor. The college's undergraduate curriculum committee then established a minor in Geriatric Engineering in 2019, as the next logical step in the process toward developing a bachelor's degree. In conclusion, student feedback from several years of Geriatric Engineering courses, data from individuals over 60 and an advisory board support the next step of creating a Geriatric Engineering Bachelor's degree. Student evaluations/perceptions of the courses were positive. Data and feedback indicate that there is a need for a dedicated undergraduate Geriatric Engineering program.

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RETRACTED ARTICLE: Deep learning model for home automation and energy reduction in a smart home environment platform

Cited by 63 publications

References 33 publications

Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events

Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events

On the rewards of self-adaptive IoT honeypots

Geriatric Engineering: An Engineering Systems Design Approach to Human Aging

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