Machine learning based activity learning for behavioral contexts in Internet of things (IoT)

Name: Machine learning based activity learning for behavioral contexts in Internet of things (IoT)
Author: Safyan, M., Sarwar, S., Ul Qayyum, Z., Iqbal, M., Li, S. C., Kashif, Muhammad

İsim	Machine learning based activity learning for behavioral contexts in Internet of things (IoT)
Yazar	Safyan, M., Sarwar, S., Ul Qayyum, Z., Iqbal, M., Li, S. C., Kashif, Muhammad
Basım Tarihi:	2020-12
Basım Yeri	- Springer Nature
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0361-7688
Kayıt Numarası	9eb56e69-1552-49f1-9d96-721a62e1a35c
Tarih	2020-12
Örnek Metin	Ontology based activity learning models play a vital role in diverse fields of Internet of Things (IoT) such as smart homes, smart hospitals or smart communities etc. The prevalent challenges with ontological models are their static nature and inability of self-evolution. The models cannot be completed at once and smart home inhabitants cannot be restricted to limit their activities. Also, inhabitants are not predictable in nature and may perform "Activities of Daily Life (ADL)" not listed in ontological model. This gives rise to the need of developing an integrated framework based on unified conceptual backbone (i.e. activity ontologies), addressing the lifecycle of activity recognition and producing behavioral models according to inhabitant's routine. In this paper, an ontology evolution process has been proposed that learns particular activities from existing set of activities in daily life (ADL). It learns new activities that have not been identified by the recognition model, adds new properties with existing activities and learns inhabitant's newest behavior of performing activities through Artificial Neural Network (ANN). The better degree of true positivity is evidence of activity recognition with detection of noisy sensor data. Effectiveness of proposed approach is evident from improved rate of activity learning, activity detection and ontology evolution.
DOI	10.1134/S0361768820080204
Cilt	46

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Machine learning based activity learning for behavioral contexts in Internet of things (IoT)

Yazar Safyan, M., Sarwar, S., Ul Qayyum, Z., Iqbal, M., Li, S. C., Kashif, Muhammad

Basım Tarihi 2020-12

Basım Yeri - Springer Nature

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0361-7688

Kayıt Numarası 9eb56e69-1552-49f1-9d96-721a62e1a35c

Tarih 2020-12

Örnek Metin Ontology based activity learning models play a vital role in diverse fields of Internet of Things (IoT) such as smart homes, smart hospitals or smart communities etc. The prevalent challenges with ontological models are their static nature and inability of self-evolution. The models cannot be completed at once and smart home inhabitants cannot be restricted to limit their activities. Also, inhabitants are not predictable in nature and may perform "Activities of Daily Life (ADL)" not listed in ontological model. This gives rise to the need of developing an integrated framework based on unified conceptual backbone (i.e. activity ontologies), addressing the lifecycle of activity recognition and producing behavioral models according to inhabitant's routine. In this paper, an ontology evolution process has been proposed that learns particular activities from existing set of activities in daily life (ADL). It learns new activities that have not been identified by the recognition model, adds new properties with existing activities and learns inhabitant's newest behavior of performing activities through Artificial Neural Network (ANN). The better degree of true positivity is evidence of activity recognition with detection of noisy sensor data. Effectiveness of proposed approach is evident from improved rate of activity learning, activity detection and ontology evolution.

DOI 10.1134/S0361768820080204

Cilt 46