Cognitive Inference Device for Activity Supervision in the Elderly

Mishra, Nilamadhab; Lin, Chung‐Chih; Chang, Hsien-Tsung

doi:10.1155/2014/125618

Cited by 9 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this phase, comprehensive computational analysis is used to empirically study the performance of our KGAC framework implementations. A number of computational intelligence approaches, such as the fuzzy approach, neural approach, type-1 neuro-fuzzy approach, and type-2 neuro-fuzzy approach, are available to implement the functional processes of KGAC framework; however, the type-2 neuro-fuzzy analytic approach is more effective in offering greater tolerance and better at addressing the uncertainties actually encountered in BI applications [ 40 – 41 ]. The functional and operational analysis of our KGAC framework is mapped to a neuro-fuzzy analytic architecture to empirically estimate the KAP.…”

Section: Analysis and Discussionmentioning

confidence: 99%

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis

2015

Self Cite

View full text Add to dashboard Cite

The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions.

show abstract

Section: Analysis and Discussionmentioning

confidence: 99%

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis

2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…Enabling the IoE based product quality assessment in an advanced BI environment creates several research challenges [6]. Here we take a small case example only to visualise the analytic functions of system.…”

Section: IVmentioning

confidence: 99%

“…The e-consumers are the real back bone of the e-BI operations. So, the consumer-end analytics are the powerful tools to analyse the current e-business trends and strategy and such analytics deeply consider the e-consumer's feedbacks on several BI parameters that are strongly related to etrading and other correlated operations [5][6]. In figure 1, we sketch an IoE A (IoE and Analytics) schema structure along with its high correlated analytic tasks that can be applied to the e-business intelligence applications.…”

mentioning

confidence: 99%

Reasonable e-Business expectancy Analytics in an IoE Edge

Mishra¹

2018

IJRASET

View full text Add to dashboard Cite

Abstract:With the rapid growth of e-Business applications, several new analytics are researched for assessing the strategy that encompasses the current BI decisions, buying policies, consumer behaviour to e-business and vice versa. The consumer-end views can be composed from their registered IoE edge point devices that are exposed for assessing e-BI strategy. The consumerend views are considered as the potential data sources that need an effective cognitive analytics system to massively turn into the e-business goldmines. The Internet of Everything motivated consumer-end analytics has much brighter prospective in order to modernise the e-business intelligence applications. In this work, we discuss an intellectual consumer-end analytic system that implements an integrated choice based problem-solving mechanism to generate the insights for the predefined e-business parameters and dynamically suggests the solutions for the prospective BI requirements. For the system implementation, we in specta case base analysis that assesses the e-BI parameters copycatted with some real values. The analysis acts appositive elucidation in small scale e-business data depository to generate potential business worth and the small scale data repository may transform into a big data-value for the modernised e-BI applications. Keywords: IoE and Analytics, consumer-end analytics, BQ (business queries) knowledge base, IoE edge, e-BI I. INTRODUCTION In the current and upcoming days, the IoE tasks are gaining popularities across various Intellectual BI applications. The main aim is to get the insights from large scale network-centric data, such as IoE data that can be used to produce intelligence for the applications. In the current Intellectual domain applications, the network-centric data are highly unstructured and ambiguous, and create research challenges in inferencing the potential knowledge. The survey reveals that time to insight is slow, quality of insight is poor, and cost of insight is high for IoE big data applications, on the other hand, those Intellectual domain applications require low cost, high quality, and real time systems and algorithms to massively transform their data into cognitive values of goldmines. Such cognitive values are utilized as knowledge and insights for creating worth of the Intellectual domain applications [1][2][3][4]. The IoE (Internet of Everything) driven consumer-end analytics has much bright prospective in order to modernise the e-business intelligence applications. The global IoE evolutionary network capable to connect billions of IoE objects for the purpose of autonomous communication. In e-business intelligence applications, the IoE and analytics play major role towards generating the business insights that enable the e-business to construct a global trends and strategy for the e-consumers. The e-consumers are the real back bone of the e-BI operations. So, the consumer-end analytics are the powerful tools to analyse the current e-business trends and strategy and such analytics deeply consider th...

show abstract

“…In this work, we propose an IKR framework for the effective transformation of a current superseded IoT knowledge base into a renovated knowledge base system that can yield better business value and be more cost effective than standardising a new system for the same purpose. The IKR framework can build, organise, and reuse the knowledge from the current superseded IoT knowledge base to provide BIservices aimed at self-regulated decisions, actuations, control, and coordinations of the things (man, machine, and process) involved in BI-services through networked IoT objects [9,10].…”

Section: Introductionmentioning

confidence: 99%

An IoT Knowledge Reengineering Framework for Semantic Knowledge Analytics for BI-Services

Mishra

Chang

Lin

2015

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

In a progressive business intelligence (BI) environment, IoT knowledge analytics are becoming an increasingly challenging problem because of rapid changes of knowledge context scenarios along with increasing data production scales with business requirements that ultimately transform a working knowledge base into a superseded state. Such a superseded knowledge base lacks adequate knowledge context scenarios, and the semantics, rules, frames, and ontology contents may not meet the latest requirements of contemporary BI-services. Thus, reengineering a superseded knowledge base into a renovated knowledge base system can yield greater business value and is more cost effective and feasible than standardising a new system for the same purpose. Thus, in this work, we propose an IoT knowledge reengineering framework (IKR framework) for implementation in a neurofuzzy system to build, organise, and reuse knowledge to provide BI-services to the things (man, machines, places, and processes) involved in business through the network of IoT objects. The analysis and discussion show that the IKR framework can be well suited to creating improved anticipation in IoT-driven BI-applications.

show abstract

Cognitive Inference Device for Activity Supervision in the Elderly

Cited by 9 publications

References 36 publications

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis

Reasonable e-Business expectancy Analytics in an IoE Edge

An IoT Knowledge Reengineering Framework for Semantic Knowledge Analytics for BI-Services

Contact Info

Product

Resources

About