B.Ashwin Gobin

University of Mauritius, Computer Science And Engineering, Faculty Member

University of Mauritius, Computer Science & Engineering, Faculty Member

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Phone: +2304037893
Address: Room 1.11
Phase II Building
Faculty Of Engineering
University of Mauritius
Reduit Mauritius

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Papers by B.Ashwin Gobin

An Approach for Conducting Educational Research on Two Multidisciplinary Areas of Information Systems; Enterprise Resource Planning and Big Data

Towards Achieving Semantic Interoperability in an IoT-enabled Smart Campus

Background: Observational clinical studies play a pivotal role in advancing medical knowledge and... more Background: Observational clinical studies play a pivotal role in advancing medical knowledge and patient healthcare. To lessen the prohibitive costs of conducting these studies and support evidence-based medicine, results emanating from these studies need to be shared and compared to one another. Current approaches for clinical study management have limitations that prohibit the effective sharing of clinical research data. Methods: The objective of this paper is to present a proposal for a clinical study architecture to not only facilitate the communication of clinical study data but also its context so that the data that is being communicated can be unambiguously understood at the receiving end. Our approach is two-fold. First we outline our methodology to map clinical data from Clinical Data Interchange Standards Consortium Operational Data Model (ODM) to the Fast Healthcare Interoperable Resource (FHIR) and outline the strengths and weaknesses of this approach. Next, we propose two FHIR-based models, to capture the metadata and data from the clinical study, that not only facilitate the syntactic but also semantic interoperability of clinical study data. Conclusions: This work shows that our proposed FHIR resources provide a good fit to semantically enrich the ODM data. By exploiting the rich information model in FHIR, we can organise clinical data in a manner that preserves its organisation but captures its context. Our implementations demonstrate that FHIR can natively manage clinical data. Furthermore, by providing links at several levels, it improves the traversal and querying of the data. The intended benefits of this approach is more efficient and effective data exchange that ultimately will allow clinicians to switch their focus back to decision-making and evidence-based medicines.

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A Voice-Based Personal Assistant for Mental Health in Kreol Morisien

Journal of Electrical and Computer Engineering, Dec 27, 2023

Forecasting Resort Hotel Tourism Demand Using Deep Learning Techniques – A Systematic Literature Review

Semantic Model for IoT-Enabled Smart Healthcare ‒ An Ontology-based Study

2023 International Conference on Inventive Computation Technologies (ICICT)

Sentiment Analysis of Social Media Comments in Mauritius

2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)

A SLR on Deep Learning Models Based on Textual Information For Effort Estimation in Scrum

Deep Learning (DL) is a branch of Machine Learning where models are developed using neural networ... more Deep Learning (DL) is a branch of Machine Learning where models are developed using neural networks made of several layers for prediction. DL models have been developed to predict effort estimation in software development. This paper presents a review of works which discuss the use of DL models for effort estimation for Scrum. The various textual information, the different DL techniques used. The methodology used for the review is snowballing. It was found that Deep-SE, a model which combines LSTM and RHN has been developed specifically for effort estimation. Also, a number of other DL techniques which have been experimented are discussed. A number of performance metrics were identified and also the perfomance of the various models were compared.

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An ICT architecture for Smart Local Councils: a Mauritian case study

International Journal of Information Technology

A Deep-Learning Framework for Analysing Students’ Review in Higher Education

Computational Intelligence and Neuroscience

As part of continuous process improvements to teaching and learning, the management of tertiary i... more As part of continuous process improvements to teaching and learning, the management of tertiary institutions requests students to review modules towards the end of each semester. These reviews capture students’ perceptions about various aspects of their learning experience. Considering the large volume of textual feedback, it is not feasible to manually analyze all the comments, hence the need for automated approaches. This study presents a framework for analyzing students’ qualitative reviews. The framework consists of four distinct components: aspect-term extraction, aspect-category identification, sentiment polarity determination, and grades’ prediction. We evaluated the framework with the dataset from the Lilongwe University of Agriculture and Natural Resources (LUANAR). A sample size of 1,111 reviews was used. A microaverage F1-score of 0.67 was achieved using Bi- LSTM-CRF and BIO tagging scheme for aspect-term extraction. Twelve aspect categories were then defined for the educ...

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KreolStem: A hybrid language-dependent stemmer for Kreol Morisien

Journal of Experimental & Theoretical Artificial Intelligence

Evaluation Metrics for Ontology Modules

2022 IEEE International Conference on Data Science and Information System (ICDSIS)

LUPOnto-An Ontology fpr Smart Land Use and Planning in Mauritian Local Councils

2022 IEEE International Conference on Data Science and Information System (ICDSIS)

A systematic literature review on semantic models for IoT-enabled smart campus

Applied Ontology

Smart communities have recently gained much attention. Researchers have been trying to tackle a n... more Smart communities have recently gained much attention. Researchers have been trying to tackle a number of challenges faced by smart communities. Interoperability is one key challenge that occurs due to different systems using different knowledge representations. To solve interoperability problems, ontologies are seen as a promising solution as they provide a commonly agreed vocabulary for representing data that are understandable by stakeholders of smart communities. Smart communities make use of Internet of Things (IoT) and ubiquitous networks to support communication among objects and devices in such environments. Smart campuses are examples of smart communities. Recently, many articles related to ontologies focusing on smart communities and smart campuses in IoT environments, have been published. This paper presents a Systematic Literature Review that has been conducted using Google Scholar. 18 ontologies for smart communities/smart campuses have been identified and analyzed out ...

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An Ontology for an IoT-enabled Smart Library in a University Campus

2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)

Enterprise Resource Planning integrated with Business Analytics in Higher Education

2022 IEEE Global Engineering Education Conference (EDUCON)

An Ontology for an IoT-enabled Smart Classroom in a University Campus

2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 2019

Smart cities represent conceptual urban development to improve people’s lives by providing them w... more Smart cities represent conceptual urban development to improve people’s lives by providing them with better and effective services. Worldwide research is currently being carried out to meet this vision. The Internet of Things (IoT) play a vital role in providing effective solutions for smart cities. An IoT-enabled smart classroom is one example of IoT application developed in the context of a smart city. The smart classroom makes use of devices and sensors to facilitate learning among learners. Interoperability is one key challenge being faced by smart applications where diverse systems have to interact with each other. The data and data format exchanged by one system often differ from other systems leading to interoperability problems. Semantic interoperability refers to unambiguous access and interpretation of data by different communicating parties, ensuring a common meaning of data exchanged. To ensure semantic interoperability, ontologies are being used to provide semantics in terms of common vocabulary and to facilitate exchange of information between different systems. This paper therefore presents an ontology for an IoT-enabled smart classroom in a university campus to handle semantic interoperability issues in a smart campus environment, taking into consideration new paradigms like the IoT and pedagogical concepts like learner analytics.

An Ontology for an IoT-enabled Smart Parking in a University Campus

2019 IEEE International Smart Cities Conference (ISC2), 2019

A lot emphasis is currently being laid worldwide on how to solve smart city problems. Researchers... more A lot emphasis is currently being laid worldwide on how to solve smart city problems. Researchers are coming up with innovative and efficient solutions to tackle smart city challenges. A university campus is like a small city and it often suffers from similar problems a smart city. Finding a parking space to park one’s vehicle has been a challenging problem whether in a city or campus. Several smart parking projects have been implemented. To solve interoperability problems between such systems and other systems in a smart community, ontologies are used to provide semantics in terms of common vocabulary and to facilitate exchange of information between diverse systems. A number of ontologies have been developed in the context of smart city but very few have focused on a smart campus. The paper therefore presents an ontology for a smart parking in a university campus taking into consideration new paradigms like the Internet of Things (IoT).

An Overview of Semantic Interoperability Ontologies and Frameworks for IoT

2018 Sixth International Conference on Enterprise Systems (ES), 2018

Interoperability is one of the key challenges that needs to be addressed to realize the vision of... more Interoperability is one of the key challenges that needs to be addressed to realize the vision of the Internet of Things (IoT). Achieving interoperability in IoT will allow billions of devices to communicate and exchange information to perform jobs. Formalized ways are required to express needs and capabilities of these devices as well as services to be offered by the devices and these can be achieved by using semantics. Semantic interoperability is thus defined as unambiguous access and interpretation of data by different stakeholders. The paper presents the challenges and current solutions of semantic interoperability. It also discusses the limitations of the current solutions in IoT and recommends a way forward.

A Smart Mobile Application for Learning English Verbs in Mauritian Primary Schools

Advances in Intelligent Systems and Computing, 2019

Learning English verbs in primary schools in Mauritius may sometimes be difficult for students as... more Learning English verbs in primary schools in Mauritius may sometimes be difficult for students as English is not their mother tongue. Integrating information technology to traditional way of teaching can help to improve the teaching process. In this paper, an AI-enabled mobile application which has been developed to help the learning Mauritian students is discussed. The application provides for personalised learning and an artificial neural network analyses the learning pattern of the students to readapt the type of learning material proposed to them. To bridge the gap due to language issues, the interaction between the students and application is in Creole. The student can say the verbs in Creole and then get the translated version in the various tenses. Instructions on how to use the application is also given in Creole. Quizzes are also given to help the learning process.

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Big Data Analytics Life cycle for Social Networks’ Posts

Advances in Intelligent Systems and Computing, 2019

Today, the amount and variety of data produced have become more complex from the expansion of new... more Today, the amount and variety of data produced have become more complex from the expansion of new services and new means to produce and share content. Large datasets from different sources and of different types are very helpful to analytics. This has given rise to the concept of Big Data Analytics. This paper focuses on the work done to develop a Big Data Analytics solution for a group of psychologists, whereby the source of data is social network posts. It gives an overview of the proposed life cycle used for the development of the solution and also explains each step through the implementation of the Big Data Analytics solution.

An Approach for Conducting Educational Research on Two Multidisciplinary Areas of Information Systems; Enterprise Resource Planning and Big Data

Towards Achieving Semantic Interoperability in an IoT-enabled Smart Campus

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A Voice-Based Personal Assistant for Mental Health in Kreol Morisien

Journal of Electrical and Computer Engineering, Dec 27, 2023

Forecasting Resort Hotel Tourism Demand Using Deep Learning Techniques – A Systematic Literature Review

Semantic Model for IoT-Enabled Smart Healthcare ‒ An Ontology-based Study

2023 International Conference on Inventive Computation Technologies (ICICT)

Sentiment Analysis of Social Media Comments in Mauritius

2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)

A SLR on Deep Learning Models Based on Textual Information For Effort Estimation in Scrum

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