Age and gender classification using brain–computer interface

Kaur, Barjinder Pal; Singh, Dinesh; Roy, Partha Pratim

doi:10.1007/s00521-018-3397-1

Cited by 53 publications

(26 citation statements)

References 35 publications

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“…The development of IoT knowledge is very much and continues to be increasingly in demand by scientists and industry, it is because of the ability of IoT to connect many devices to be able to communicate with each other and enter information through the internet as delivered by (Kaur et al., 2018) even with a variety of different (heterogeneous) devices that can carry certain functions or benefits (Chan, 2015). Based on its function then IoT has a role, among others identity, track and traceability , and authentication (Liu et al., 2017), then traceability and visibility (Meng et al., 2017).…”

Section: Resultsmentioning

confidence: 99%

“…IoT research in the health sector, it is important to pay attention to various attributes of health information such as the service provider profession, task discussion room, devices used, expert support, and various personal medical data (Kim and Kim, 2018). Various medical data can be obtained by utilizing IoT devices that are in our daily lives, such as jam, health tape, scale, TV, lights, and door lock (Kaur et al., 2018), or various types of related sensors used such as smart accelerometers, gyroscope, pulse oximeter, RBG camera, camera Kinect, micro cellphone, PIR, RFID, smart tiles, etc., where these sensors are integrated with IoT technology (Santhoshi and Thirugnanam, 2016) and all these sensors can support, personal health records and self-diagnoses (Kim and Chung, 2017), for example, IoT to monitor human physiological conditions indefinitely, so doctors examine their patients remotely such as monitoring using brain safety research obtained through sensors (Kaur et al., 2018).…”

Section: Resultsmentioning

confidence: 99%

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Knowledge growth and development: internet of things (IoT) research, 2006–2018

Dachyar

Zagloel

Saragih

2019

Heliyon

101

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The term "Internet of Things" first appeared in publication paper since 2006, describing the paradigm of evolution concept that brought about by the presence of internet technology (Vermesan and Friess, 2015) which is very important in contemporary circumstances. This study conducted an in-depth analysis of the research material written on 26420 papers which focused on the published Internet of Things (IoT) research, starting from the firstly year IoT keyword appeared in 2006 until 2018. The selected paper is a combination of various disciplines and publications which are all indexed by Scopus wherein the article discusses IoT. IoT articles are classified using key attributes in sequence: the methodology used, general knowledge and applied concepts, and various general exploration topics. By using the Scientometrics method, this method will group the overall terms that appear frequently from the Scopus paper database according to keywords, titles, and abstracts. The resulting data is then studied to understand and distinguish trends that occur in the time span along with the general characteristics of the paper, in the mathematics visual scheme. All various issues that are considered in the paper's methodology selection, their studied and services innovations, and continuing discoveries on the characteristics, concepts, and processes applied to IoT success. Although it only involves scopus indexed paper, this study found a remarkable increase in the number of articles on IoT in each category of the paper. This study also reveals the direction of the regular discipline of knowledge. The use of the Scientometrics method makes the analysis able to focus on the movement of characteristics and IoT themes to researcher's direction that has not found at this time, as a comprehensive guide to further research and industry strategy that is more directed on concepts that support the 4th industrial revolution.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Knowledge growth and development: internet of things (IoT) research, 2006–2018

Dachyar

Zagloel

Saragih

2019

Heliyon

101

View full text Add to dashboard Cite

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“…Авторы обнаружили, что пожилые люди описывают положительные эмоции лучше, чем отрицательные, по сравнению с более молодыми людьми. Циммерман и соавторы [16] исследовали тональность в зависимости от возраста и пола [17]. Результаты показали, что люди в подростковом возрасте демонстрировали наименьшую эмоциональную регуляцию по сравнению с другими возрастными группами.…”

Section: Intelligent Methods Of Knowledge Extraction Based On Sentimenunclassified

Intelligent Method of Knowledge Extraction Based on Sentiment Analysis

Gerasimenko¹,

Стеценко²

2020

IZVESTIYA SFedU. ENGINEERING SCIENCES

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“…A detailed review of BCI signaling is given in Reference [78]. Convolutional neural network (CNN) architectures in Reference [79] are used to extract temporal information about the brain through BCIs, while age and gender classification with BCI is proposed in Reference [80].…”

Section: Previous Workmentioning

confidence: 99%

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

et al. 2020

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Cognitive maps are high level representations of the key topological attributes of real or abstract spatial environments progressively built by a sequence of noisy observations. Currently such maps play a crucial role in cognitive sciences as it is believed this is how clusters of dedicated neurons at hippocampus construct internal representations. The latter include physical space and, perhaps more interestingly, abstract fields comprising of interconnected notions such as natural languages. In deep learning cognitive graphs are effective tools for simultaneous dimensionality reduction and visualization with applications among others to edge prediction, ontology alignment, and transfer learning. Fuzzy cognitive graphs have been proposed for representing maps with incomplete knowledge or errors caused by noisy or insufficient observations. The primary contribution of this article is the construction of cognitive map for the sixteen Myers-Briggs personality types with a tensor distance metric. The latter combines two categories of natural language attributes extracted from the namesake Kaggle dataset. To the best of our knowledge linguistic attributes are separated in categories. Moreover, a fuzzy variant of this map is also proposed where a certain personality may be assigned to up to two types with equal probability. The two maps were evaluated based on their topological properties, on their clustering quality, and on how well they fared against the dataset ground truth. The results indicate a superior performance of both maps with the fuzzy variant being better. Based on the findings recommendations are given for engineers and practitioners.

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Age and gender classification using brain–computer interface

Cited by 53 publications

References 35 publications

Knowledge growth and development: internet of things (IoT) research, 2006–2018

Knowledge growth and development: internet of things (IoT) research, 2006–2018

Intelligent Method of Knowledge Extraction Based on Sentiment Analysis

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

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