Unsupervised Machine Learning Based Scalable Fusion for Active Perception

Jayaratne, Madhura; Silva, Daswin De; Alahakoon, Damminda

doi:10.1109/tase.2019.2910508

Cited by 19 publications

(9 citation statements)

References 42 publications

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“…It was tested on a large multimodal dataset of human activity sensors to show efficient representation and multimodal fusion for active perception. Additionally, the good cluster quality findings indicate that multimodal fusion improved the outcomes [22].…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 90%

Usages of Spark Framework with Different Machine Learning Algorithms

Mohamed

El-Henawy

Salah

2021

Computational Intelligence and Neuroscience

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Sensors, satellites, mobile devices, social media, e-commerce, and the Internet, among others, saturate us with data. The Internet of Things, in particular, enables massive amounts of data to be generated more quickly. The Internet of Things is a term that describes the process of connecting computers, smart devices, and other data-generating equipment to a network and transmitting data. As a result, data is produced and updated on a regular basis to reflect changes in all areas and activities. As a consequence of this exponential growth of data, a new term and idea known as big data have been coined. Big data is required to illuminate the relationships between things, forecast future trends, and provide more information to decision-makers. The major problem at present, however, is how to effectively collect and evaluate massive amounts of diverse and complicated data. In some sectors or applications, machine learning models are the most frequently utilized methods for interpreting and analyzing data and obtaining important information. On their own, traditional machine learning methods are unable to successfully handle large data problems. This article gives an introduction to Spark architecture as a platform that machine learning methods may utilize to address issues regarding the design and execution of large data systems. This article focuses on three machine learning types, including regression, classification, and clustering, and how they can be applied on top of the Spark platform.

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Section: Computational Intelligence and Neurosciencementioning

confidence: 90%

Usages of Spark Framework with Different Machine Learning Algorithms

Mohamed

El-Henawy

Salah

2021

Computational Intelligence and Neuroscience

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“…This was extended to fuse multimodal sensory information by proposing a multisensory self-organizing neural architecture by (Jayaratne et al 2019). This consists of GSOM layers for learning individual modalities.…”

Section: Gsom Fusionmentioning

confidence: 99%

“…The proposed self-building AI framework is primarily focused on smart cities in this paper that can be materialized for Big Data driven traffic management systems (Bandaragoda et al 2020;Nallaperuma et al 2019), intelligent video surveillance in public places/ pedestrian walks with capability to detect anomalies (Nawaratne et al 2019a(Nawaratne et al , 2019b(Nawaratne et al , 2019c, recognize human actions (Nawaratne et al 2019a(Nawaratne et al , 2019b(Nawaratne et al , 2019c, summarise surv e i l l a n c e v i d e o t o d e t e c t u n u s u a l b e h a v i o u r (Gunawardena et al 2020), intelligent energy meter reading for smart energy (Silva et al 2011) and digital health (Carey et al 2019) in smart city environments. In addition, the proposed self-building AI framework can even be extend to develop resource-efficient computing infrastructure to support effective implementation of smart cities (Jayaratne et al 2019;Kleyko et al 2019).…”

Section: Practical Implicationsmentioning

confidence: 99%

Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities

Alahakoon

Nawaratne

et al. 2020

Inf Syst Front

Self Cite

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The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the selfbuilding AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications.

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“…An unsupervised machine learning based scalable fusion model for active perception has been proposed in [133], [134], [135].…”

Section: A Advances In Researchmentioning

confidence: 99%

Toward Intelligent Industrial Informatics: A Review of Current Developments and Future Directions of Artificial Intelligence in Industrial Applications

Silva

Sierla

Alahakoon

et al. 2020

EEE Ind. Electron. Mag.

Self Cite

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Research, the universal pursuit of new knowledge, is embarking on a fresh journey into Artificial Intelligence (AI). Nature reports that AI arose nine places to fourth most popular search-term, and that search-terms machine-learning and deep-learning appeared in the top-20 search for the first time in 2018. It is pertinent for Industrial Informatics to embrace this renewed surge of interest in AI with clear direction and purpose that engages scholars, practitioners and professionals alike. This article aims to

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Unsupervised Machine Learning Based Scalable Fusion for Active Perception

Cited by 19 publications

References 42 publications

Usages of Spark Framework with Different Machine Learning Algorithms

Usages of Spark Framework with Different Machine Learning Algorithms

Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities

Toward Intelligent Industrial Informatics: A Review of Current Developments and Future Directions of Artificial Intelligence in Industrial Applications

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