Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems.

This research aims to explore more efficient machine learning (ML) algorithms with better performance for short-term forecasting. Up-to-date literature shows a lack of research on selecting practical ML algorithms for short-term forecasting in real-time industrial applications. This research uses a quantitative and qualitative mixed method combining two rounds of literature reviews, a case study, and a comparative analysis.

BioChainReward: A Secure and Incentivised Blockchain Framework for Biomedical Data Sharing.

In the era of digital healthcare, biomedical data sharing is of paramount importance for the advancement of research and personalised healthcare. However, sharing such data while preserving user privacy and ensuring data security poses significant challenges. This paper introduces BioChainReward (BCR), a blockchain-based framework designed to address these concerns.

An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework.

Among all the gas disasters, gas concentration exceeding the threshold limit value (TLV) has been the leading cause of accidents. However, most systems still focus on exploring the methods and framework for avoiding reaching or exceeding TLV of the gas concentration from viewpoints of impacts on geological conditions and coal mining working-face elements. The previous study developed a Trip-Correlation Analysis Theoretical Framework and found strong correlations between gas and gas, gas and temperature, and gas and wind in the gas monitoring system.

Using multi-focus group method as an effective tool for eliciting business system requirements: Verified by a case study.

This research aims to explore the multi-focus group method as an effective tool for systematically eliciting business requirements for business information system (BIS) projects. During the COVID-19 crisis, many businesses plan to transform their businesses into digital businesses. Business managers face a critical challenge: they do not know much about detailed system requirements and what they want for digital transformation requirements.

Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction.

Disease risk prediction is a rising challenge in the medical domain. Researchers have widely used machine learning algorithms to solve this challenge. The k-nearest neighbour (KNN) algorithm is the most frequently used among the wide range of machine learning algorithms.

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement.

Background: As the world's largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks.

Intelligent type 2 diabetes risk prediction from administrative claim data.

Type 2 diabetes is a chronic, costly disease and is a serious global population health problem. Yet, the disease is well manageable and preventable if there is an early warning. This study aims to apply supervised machine learning algorithms for developing predictive models for type 2 diabetes using administrative claim data.

A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services.

This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication.