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Trust is at the core of any payment method and it plays a crucial role in the adoption of new payment systems. Individuals need to trust in one another in order to validate any monetary system developed. With the evolution of the payment methods, shifting from paper-based to electronic-based, build consumers' trust has become a vital element for the success of businesses in the payment systems. In this sense, the aim of this paper is to provide a scoping review of the relevant literature conducted between 2013 and 2017 on mobile payment systems (MPS) sector and point out which factors might affect trust and, in turn, might have influence on MPS adoption by users according to academics. In order to organise the recent studies for further discussion, this paper proposes to divide the studies into five distinct categories: country (where the study took place), method employed (surveys, interviews etc), number of respondents, focus of the research (consumers, merchants or users in general) and model (framework) used. The findings of 76 studies (2013-2017) pointed out that there are 111 different factors that have impact on mobile payment systems, according to the authors. In addition, the findings have indicated that China is the country with more studies conducted on the adoption of MPS, followed by Spain. It is worth mentioning that, in many countries the number of studies seems underrepresented and in others, there are no data published yet. [ABSTRACT FROM AUTHOR]

The emergence of Cloud based services in recent years has sparked interest from a range of key stakeholders, including business organizations, institutions, the general public, and government. This interest is fuelled by promised new economic models driven by Cloud based services. It is a paradigm shift from heavily investing in physical IT infrastructure and resources that are internally managed and owned by organizations to pay per use for IT infrastructure and services provided by a Cloud based service suppliers. India is emerging at a rapid pace in the information technology field, thus showing high potential for the development and utilization of Cloud based services. In this research, factors that potentially have an effect on Indian based SMEs which have the intention of using Cloud based services are taken primarily from the innovation characteristic dimensions of Diffusion of Innovation (DOI) theory. The innovation characteristics identified in this paper as key factors influencing the intention to adopt Cloud based services are (1) relative advantage (2) compatibility (3) complexity (4) cost saving and (5) security. These five factors are hypothesized as having a direct effect on SMEs adoption of Cloud based services. Through the hypothesis testing, the paper concludes the following: Cloud based services adoption depended on the innovation factors namely, relative advantage, compatibility, cost, and security. [ABSTRACT FROM AUTHOR]

The aim of this paper is to demonstrate the research project proposing a policy development plan for the gold trading market in Thailand. This policy development plan will improve efficiency and the potential of the market. The reason this market must be developed is that the second major gold rush in Thai history occurred around 2008 and this completely changed the market structure. However, the market still has many problems to be resolved in order to successfully change the structure. Around 2008, the world gold price rocketed. This cycle not only caused gold price fluctuations in almost every country around the world, but created a gold rush in many countries including Thailand. More than in any other previous gold cycle period, these busts and booms in the gold price were a significant factor driving the huge amounts of gold imports into Thailand. Since then, apart from the role of gold as a traditional gift for Chinese New Year and weddings, gold plays another significant role as an investment asset. The policy development plan will not only resolve market problems, but also fulfill market requirements. In the last decade it appeared that the purpose of gold trading in Thailand was more concerned with speculation rather than the traditional reasons for consumption. This research used a qualitative methodology because it allowed the researcher to use the interpretivist paradigm. Case studies were used as a strategy of enquiry to gather in-depth information from participant interviews; it was not numerical data generated by quantitative methodology. The research selected five small groups of participants (35 people). The qualitative methodology combined results from primary and secondary research. Primary research based on interview results represented six themes and the secondary research was based on the literature review that represented eight key aspects. This paper investigates its value as capital and the impact on the culture in the Thai gold market. It was responsible for introducing modern gold trading products such as futures trading and online trading. It influenced the view of both the Sino-Thai and Indigenous Thai cultures on gold as well as impacting on global gold trading many other countries. The research results show that stabilisation and integration policies are appropriate to use in relation to the Thai gold trading industry. These two policies create a strategy to develop Thai gold trading and a plan that can be adopted by the Thai government to resolve the five significant market problems and at the same time, strengthen the Thai gold market. The results also show that physical gold trading (bullion and jewellery) has always been the most popular form of trading. For the first time in the history of the Thai gold market, physical gold bullion has overtaken the physical gold and jewellery market. Similarly, gold futures trading and online trading increased the options available to investors. The key aspects of the research outcome also demonstrate the relationship between the characteristics of the Sino-Thais and Indigenous Thais. This paper also examines how the change in the Thai gold market impacts on Thailand's economy. [ABSTRACT FROM AUTHOR]

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. Ten widely used ML algorithms are selected to conduct a comparative study of gas warning systems in a case study mine. We propose a new assessment visualization tool: a 2D space-based quadrant diagram can be used to visually map prediction error assessment and predictive performance assessment for tested algorithms. Overall, this visualization tool indicates that LR, RF, and SVM are more efficient ML algorithms with overall prediction performance for short-term forecasting. This research indicates ten tested algorithms can be visually mapped onto optimal (LR, RF, and SVM), efficient (ARIMA), suboptimal (BP-SOG, KNN, and Perceptron), and inefficient algorithms (RNN, BP_Resilient, and LSTM). The case study finds results that differ from previous studies regarding the ML efficiency of ARIMA, KNN, LR, LSTM, and SVM. This study finds different views on the prediction performance of a few paired algorithms compared with previous studies, including RF and LR, SVM and RF, KNN and ARIMA, KNN and SVM, RNN and ARIMA, and LSTM and SVM. This study also suggests that ARIMA, KNN, LR, and LSTM should be investigated further with additional prediction error assessments. Overall, no single algorithm can fit all applications. This study raises 20 valuable questions for further research. [ABSTRACT FROM AUTHOR]

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. Ten widely used ML algorithms are selected to conduct a comparative study of gas warning systems in a case study mine. We propose a new assessment visualization tool: a 2D space-based quadrant diagram can be used to visually map prediction error assessment and predictive performance assessment for tested algorithms. Overall, this visualization tool indicates that LR, RF, and SVM are more efficient ML algorithms with overall prediction performance for short-term forecasting. This research indicates ten tested algorithms can be visually mapped onto optimal (LR, RF, and SVM), efficient (ARIMA), suboptimal (BP-SOG, KNN, and Perceptron), and inefficient algorithms (RNN, BP_Resilient, and LSTM). The case study finds results that differ from previous studies regarding the ML efficiency of ARIMA, KNN, LR, LSTM, and SVM. This study finds different views on the prediction performance of a few paired algorithms compared with previous studies, including RF and LR, SVM and RF, KNN and ARIMA, KNN and SVM, RNN and ARIMA, and LSTM and SVM. This study also suggests that ARIMA, KNN, LR, and LSTM should be investigated further with additional prediction error assessments. Overall, no single algorithm can fit all applications. This study raises 20 valuable questions for further research. [ABSTRACT FROM AUTHOR]

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. However, this framework's effectiveness must be examined to determine whether it might be adopted in other coal mine cases. This research aims to explore a proposed verification analysis approach—First-round—Second-round—Verification round (FSV) analysis approach to verify the robustness of the Trip-Correlation Analysis Theoretical Framework for developing a gas warning system. A mixed qualitative and quantitative research methodology is adopted, including a case study and correlational research. The results verify the robustness of the Triple-Correlation Analysis Theoretical Framework. The outcomes imply that this framework is potentially valuable for developing other warning systems. The proposed FSV approach can also be used to explore data patterns insightfully and offer new perspectives to develop warning systems for different industry applications. [ABSTRACT FROM AUTHOR]

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. Among many approaches used for understanding business requirements, the focus group method has been used to help elicit BIS needs over the past 30 years. However, most focus group studies about research practices mainly focus on a particular disciplinary field, such as social, biomedical, and health research. Limited research reported using the multi-focus group method to elicit business system requirements. There is a need to fill this research gap. A case study is conducted to verify that the multi-focus group method might effectively explore detailed system requirements to cover the Case Study business's needs from transforming the existing systems into a visual warning system. The research outcomes verify that the multi-focus group method might effectively explore the detailed system requirements to cover the business's needs. This research identifies that the multi-focus group method is especially suitable for investigating less well-studied, no previous evidence, or unstudied research topics. As a result, an innovative visual warning system was successfully deployed based on the multi-focus studies for user acceptance testing in the Case Study mine in Feb 2022. The main contribution is that this research verifies the multi-focus group method might be an effective tool for systematically eliciting business requirements. Another contribution is to develop a flowchart for adding to Systems Analysis & Design course in information system education, which may guide BIS students step by step on using the multi-focus group method to explore business system requirements in practice. [ABSTRACT FROM AUTHOR]

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. Following guidelines from the Elixhauser Comorbidity Index, 31 variables were considered. Five supervised machine learning algorithms were used for developing type 2 diabetes prediction models. Principal component analysis was applied to rank variables' importance in predictive models. Random forest (RF) showed the highest accuracy (85.06%) among the algorithms, closely followed by the k-nearest neighbor (84.48%). The analysis further revealed RF as a high performing algorithm irrespective of data imbalance. As revealed by the principal component analysis, patient age is the most important predictor for type 2 diabetes, followed by a comorbid condition (i.e., solid tumor without metastasis). This study's finding of RF as the best performing classifier is consistent with the promise of tree-based algorithms for public data in other works. Thus, the outcome can guide in designing automated surveillance of patients at risk of forming diabetes from administrative claim information and will be useful to health regulators and insurers. [ABSTRACT FROM AUTHOR]

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. This paper presents a study on different KNN variants (Classic one, Adaptive, Locally adaptive, k-means clustering, Fuzzy, Mutual, Ensemble, Hassanat and Generalised mean distance) and their performance comparison for disease prediction. This study analysed these variants in-depth through implementations and experimentations using eight machine learning benchmark datasets obtained from Kaggle, UCI Machine learning repository and OpenML. The datasets were related to different disease contexts. We considered the performance measures of accuracy, precision and recall for comparative analysis. The average accuracy values of these variants ranged from 64.22% to 83.62%. The Hassanaat KNN showed the highest average accuracy (83.62%), followed by the ensemble approach KNN (82.34%). A relative performance index is also proposed based on each performance measure to assess each variant and compare the results. This study identified Hassanat KNN as the best performing variant based on the accuracy-based version of this index, followed by the ensemble approach KNN. This study also provided a relative comparison among KNN variants based on precision and recall measures. Finally, this paper summarises which KNN variant is the most promising candidate to follow under the consideration of three performance measures (accuracy, precision and recall) for disease prediction. Healthcare researchers and stakeholders could use the findings of this study to select the appropriate KNN variant for predictive disease risk analytics. [ABSTRACT FROM AUTHOR]

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. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. Methods: Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. Results: The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. Conclusions: The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments. [ABSTRACT FROM AUTHOR]

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. The framework uses a fusion of electrocardiogram (ECG) and photoplethysmogram (PPG) signals when performing authentication. In addition to relying on the unique identification characteristics of the users' biometric traits, the security of the framework is empowered by the use of Homomorphic Encryption (HE). The use of HE allows patients' data to stay encrypted when being processed or analyzed in the cloud. Thus, providing not only a fast and reliable authentication mechanism, but also closing the door to many traditional security attacks. The framework's performance was evaluated and validated using a machine learning (ML) model that tested the framework using a dataset of 25 users in seating positions. Compared to using just ECG or PPG signals, the results of using the proposed fused-based biometric framework showed that it was successful in identifying and authenticating all 25 users with 100% accuracy. Hence, offering some significant improvements to the overall security and privacy of personalized healthcare systems. [ABSTRACT FROM AUTHOR]