RML based ontology development approach in internet of things for healthcare domain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jameel Ahamed ◽  
Roohie Naaz Mir ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Internet Of Things ◽  
Relational Database ◽  
Vascular Diseases ◽  
Heterogeneous Data ◽  
Semantic Interoperability ◽  
Ontology Development ◽  
Content Type ◽  
Cardio Vascular ◽  
Diverse Data ◽  
Better Than

Purpose A huge amount of diverse data is generated in the Internet of Things (IoT) because of heterogeneous devices like sensors, actuators, gateways and many more. Due to assorted nature of devices, interoperability remains a major challenge for IoT system developers. The purpose of this study is to use mapping techniques for converting relational database (RDB) to resource directory framework (RDF) for the development of ontology. Ontology helps in achieving semantic interoperability in application areas of IoT which results in shared/common understanding of the heterogeneous data generated by the diverse devices used in health-care domain. Design/methodology/approach To overcome the issue of semantic interoperability in healthcare domain, the authors developed an ontology for patients having cardio vascular diseases. Patients located at any place around the world can be diagnosed by Heart Experts located at another place by using this approach. This mechanism deals with the mapping of heterogeneous data into the RDF format in an integrated and interoperable manner. This approach is used to integrate the diverse data of heart patients needed for diagnosis with respect to cardio vascular diseases. This approach is also applicable in other fields where IoT is mostly used. Findings Experimental results showed that the RDF works better than the relational database for semantic interoperability in the IoT. This concept-based approach is better than key-based approach and reduces the computation time and storage of the data. Originality/value The proposed approach helps in overcoming the demerits of relational database like standardization, expressivity, provenance and supports SPARQL. Therefore, it helps to overcome the heterogeneity, thereby enabling the semantic interoperability in IoT.

Attack detection in medical Internet of things using optimized deep learning: enhanced security in healthcare sector

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
J Aruna Santhi ◽  
G Vijaya Saradhi
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Hybrid Algorithm ◽  
Attack Detection ◽  
Hospital Environment ◽  
Healthcare Sector ◽  
Content Type ◽  
Accurate Detection ◽  
Iot Devices ◽  
Better Than

PurposeThis paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.Design/methodology/approachThis paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.FindingsFrom the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.Originality/valueThis paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.

Working framework of semantic interoperability for CRIS with heterogeneous data sources

2017 ◽  
Vol 73 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Amed Leiva-Mederos ◽  
Jose A. Senso ◽  
Yusniel Hidalgo-Delgado ◽  
Pedro Hipola
Keyword(s):  
Heterogeneous Data ◽  
Semantic Interoperability ◽  
Data Sets ◽  
Research Information ◽  
Data Set ◽  
Content Type ◽  
Proposed Model ◽  
Semantic Alignment ◽  
Technical Specifications ◽  
Open Archives

Purpose Information from Current Research Information Systems (CRIS) is stored in different formats, in platforms that are not compatible, or even in independent networks. It would be helpful to have a well-defined methodology to allow for management data processing from a single site, so as to take advantage of the capacity to link disperse data found in different systems, platforms, sources and/or formats. Based on functionalities and materials of the VLIR project, the purpose of this paper is to present a model that provides for interoperability by means of semantic alignment techniques and metadata crosswalks, and facilitates the fusion of information stored in diverse sources. Design/methodology/approach After reviewing the state of the art regarding the diverse mechanisms for achieving semantic interoperability, the paper analyzes the following: the specific coverage of the data sets (type of data, thematic coverage and geographic coverage); the technical specifications needed to retrieve and analyze a distribution of the data set (format, protocol, etc.); the conditions of re-utilization (copyright and licenses); and the “dimensions” included in the data set as well as the semantics of these dimensions (the syntax and the taxonomies of reference). The semantic interoperability framework here presented implements semantic alignment and metadata crosswalk to convert information from three different systems (ABCD, Moodle and DSpace) to integrate all the databases in a single RDF file. Findings The paper also includes an evaluation based on the comparison – by means of calculations of recall and precision – of the proposed model and identical consultations made on Open Archives Initiative and SQL, in order to estimate its efficiency. The results have been satisfactory enough, due to the fact that the semantic interoperability facilitates the exact retrieval of information. Originality/value The proposed model enhances management of the syntactic and semantic interoperability of the CRIS system designed. In a real setting of use it achieves very positive results.

Methodology and mechanisms for federation of heterogeneous metadata sources and ontology development in emerging collaborative environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Beibei Pang ◽  
Juanqiong Gou ◽  
Hamideh Afsarmanesh ◽  
Wenxin Mu ◽  
Zuopeng Zhang
Keyword(s):  
Leading Edge ◽  
Heterogeneous Data ◽  
Ontology Development ◽  
Content Type ◽  
Collaborative Environment ◽  
Ontology Language ◽  
Heterogeneous Data Sources ◽  
Information And Communication ◽  
Higher Education In China ◽  
And Behavior

Purpose Leading-edge information and communication technology provides the base to facilitate obtaining, interoperating and federating shared metadata knowledge in collaborative networks from multiple heterogeneous data sources. The purpose of this study is to develop a methodology and a set of mechanisms to support this task in the collaborative environment. Design/methodology/approach In this paper, the authors first identify and capture four main typical sources to find or generate metadata knowledge for shared data in emerging networked environments, including existing well-designed metadata, the typical ones are relational schemas of existing databases in the environment; fragmented metadata sources, i.e. metadata that can be realized from existing mission statements and example application scenarios in the environment, usually characterized by their fragmented, lightweight and behavior-intensive features; extracting metadata for simple labeled unstructured data, e.g. textual communications among its stakeholders; and semantic constraints on metadata, e.g. the temporal data behavior could be generated from governance policies in the environment. Second, the authors introduce their systematic methodology to the unification of the resulted metadata consisting of four semiautomated unification steps that gradually develops and enhances a unified ontology for the environment, formalized in web ontology language. Findings The methodology steps and their corresponding mechanisms are described and exemplified in detail in this paper. Furthermore, this paper presents the outcome of applying the authors’ methodology to an example emerging case through the generation of a unified ontology for that environment. Originality/value The addressed example application area is a real case in the field of higher education in China and therefore serves as a proof of concept and verification of the effectiveness of the authors’ proposed approach.

Role of Emotional Processes in Cardio-vascular Diseases

2012 ◽  
Author(s):  
Suman Balhara ◽  
Nov Rattan Sharma ◽  
Amrita Yadav
Keyword(s):  
Vascular Diseases ◽  
Emotional Processes ◽  
Cardio Vascular

A Review on Heart Disease Detection Techniques

2017 ◽  
Vol 7 (7) ◽  
pp. 395
Author(s):  
. Anika ◽  
Navpreet Kaur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Vascular Diseases ◽  
Disease Detection ◽  
Automated Classification ◽  
Feature Extraction Method ◽  
Detection Techniques ◽  
Cardio Vascular ◽  
Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

2021 ◽  
Vol 21 (3) ◽  
pp. 1-22
Author(s):  
Celestine Iwendi ◽  
Saif Ur Rehman ◽  
Abdul Rehman Javed ◽  
Suleman Khan ◽  
Gautam Srivastava
Keyword(s):  
Internet Of Things ◽  
Detection System ◽  
Experimental Testing ◽  
Cyber Attacks ◽  
Internet Technology ◽  
The Internet ◽  
Business Operations ◽  
Private Data ◽  
The Internet Of Things ◽  
Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

Nature-inspired hybrid deep learning for race detection by face shape features

2020 ◽  
Vol 13 (3) ◽  
pp. 365-388
Author(s):  
Asha Sukumaran ◽  
Thomas Brindha
Keyword(s):  
Feature Extraction ◽  
Hybrid Algorithm ◽  
Shape Features ◽  
Error Performance ◽  
Color Feature ◽  
Content Type ◽  
Dragonfly Algorithm ◽  
Face Shape ◽  
Race Detection ◽  
Better Than

PurposeThe humans are gifted with the potential of recognizing others by their uniqueness, in addition with more other demographic characteristics such as ethnicity (or race), gender and age, respectively. Over the decades, a vast count of researchers had undergone in the field of psychological, biological and cognitive sciences to explore how the human brain characterizes, perceives and memorizes faces. Moreover, certain computational advancements have been developed to accomplish several insights into this issue.Design/methodology/approachThis paper intends to propose a new race detection model using face shape features. The proposed model includes two key phases, namely. (a) feature extraction (b) detection. The feature extraction is the initial stage, where the face color and shape based features get mined. Specifically, maximally stable extremal regions (MSER) and speeded-up robust transform (SURF) are extracted under shape features and dense color feature are extracted as color feature. Since, the extracted features are huge in dimensions; they are alleviated under principle component analysis (PCA) approach, which is the strongest model for solving “curse of dimensionality”. Then, the dimensional reduced features are subjected to deep belief neural network (DBN), where the race gets detected. Further, to make the proposed framework more effective with respect to prediction, the weight of DBN is fine tuned with a new hybrid algorithm referred as lion mutated and updated dragon algorithm (LMUDA), which is the conceptual hybridization of lion algorithm (LA) and dragonfly algorithm (DA).FindingsThe performance of proposed work is compared over other state-of-the-art models in terms of accuracy and error performance. Moreover, LMUDA attains high accuracy at 100th iteration with 90% of training, which is 11.1, 8.8, 5.5 and 3.3% better than the performance when learning percentage (LP) = 50%, 60%, 70%, and 80%, respectively. More particularly, the performance of proposed DBN + LMUDA is 22.2, 12.5 and 33.3% better than the traditional classifiers DCNN, DBN and LDA, respectively.Originality/valueThis paper achieves the objective detecting the human races from the faces. Particularly, MSER feature and SURF features are extracted under shape features and dense color feature are extracted as color feature. As a novelty, to make the race detection more accurate, the weight of DBN is fine tuned with a new hybrid algorithm referred as LMUDA, which is the conceptual hybridization of LA and DA, respectively.

A four-step blueprint for digital reinvention

2016 ◽  
Vol 44 (4) ◽  
pp. 18-25 ◽  
Author(s):  
Saul J. Berman ◽  
Peter J. Korsten ◽  
Anthony Marshall
Keyword(s):  
Internet Of Things ◽  
Design Methodology ◽  
The Internet ◽  
Content Type ◽  
New Ways Of Working ◽  
Partner Relationships ◽  
Strategic Focus ◽  
The Internet Of Things ◽  
Practical Implications ◽  
Customer Need

Purpose Digital reinvention helps organizations create unique, compelling experiences for their customers, partners, employees and other stakeholders. Design/methodology/approach Digital reinvention combines the capabilities of multiple technologies, including cloud, cognitive, mobile and the Internet of Things (IoT) to rethink customer and partner relationships from a perspective of fundamental customer need, use or aspiration. Findings The most successful digitally reinvented businesses establish a platform of engagement for their customers, with the business acting as enabler, conduit and partner Practical implications For successful digital reinvention, organizations need to pursue a new strategic focus, build new expertise and establish new ways of working. Originality/value The article offers a blueprint for digital reinvention that involves rethinking customer and partner relationships from a perspective of fundamental customer need, use or aspiration.

Current CITE-ings from the popular and trade computing press: telework and telecommuting

2016 ◽  
Vol 33 (1) ◽  
pp. 21-22 ◽  
Author(s):  
Martin Kesselman
Keyword(s):  
Literature Review ◽  
Design Methodology ◽  
Virtual Reference ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Better Than

Purpose – This article examines Current CITE-ings from the Popular and Trade Computing Press, Telework and Telecommuting Design/methodology/approach – The methodology adopted is a literature review. Findings – Readily available technologies now allow librarians to perform most of their work-offsite. Some traditional building-based services such as reference, have been taken over by virtual reference and now even instruction offers options on par with or even better than classroombased questions such as a webinar that can be viewed and reviewed at any time or by having librarians embedded into various courseware packages. Researchlimitations/implications – Librarians no longer need be limited to a single library; groups of subject librarians can work together in the cloud to provide services to multiple universities. Originality/value – This article collates some articles from the non-library literature that mayprovide some ideas and review advantages and disadvantages for both the library and employee

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