Public Sentiment Analysis and Topic Modeling Regarding COVID-19’s Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia

[Abstract] The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown as reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.Taiwan. Ministry of Science and Technology; 108-2511-H-224-007-MY

A Systematic Review of Trustworthy and Explainable Artificial Intelligence in Healthcare: Assessment of Quality, Bias Risk, and Data Fusion

El trabajo forma parte de un desarrollo conjunto por parte del Dr. Laith Alzubaidi, el Dr. José Santamaria (el Dr. Santamaría realizó tareas de co-supervisión de la tesis doctoral del Dr. Alzubaidi), y otros investigadores. La aportación de los Drs. José Santamaría y Laith Alzubaidi consistió en profundizar aún más en el estudio de aspectos tales como la explicabilidad y confiabilidad de los modelos de aprendizaje vistos en la tesis doctoral del Dr. Alzubaidi con repercusión en el ámbito de la salud.In the last few years, the trend in health care of embracing artificial intelligence (AI) has dramatically changed the medical landscape. Medical centres have adopted AI applications to increase the accuracy of disease diagnosis and mitigate health risks. AI applications have changed rules and policies related to healthcare practice and work ethics. However, building trustworthy and explainable AI (XAI) in healthcare systems is still in its early stages. Specifically, the European Union has stated that AI must be human-centred and trustworthy, whereas in the healthcare sector, low methodological quality and high bias risk have become major concerns. This study endeavours to offer a systematic review of the trustworthiness and explainability of AI applications in healthcare, incorporating the assessment of quality, bias risk, and data fusion to supplement previous studies and provide more accurate and definitive findings. Likewise, 64 recent contributions on the trustworthiness of AI in healthcare from multiple databases (i.e., ScienceDirect, Scopus, Web of Science, and IEEE Xplore) were identified using a rigorous literature search method and selection criteria. The considered papers were categorised into a coherent and systematic classification including seven categories: explainable robotics, prediction, decision support, blockchain, transparency, digital health, and review. In this paper, we have presented a systematic and comprehensive analysis of earlier studies and opened the door to potential future studies by discussing in depth the challenges, motivations, and recommendations. In this study a systematic science mapping analysis in order to reorganise and summarise the results of earlier studies to address the issues of trustworthiness and objectivity was also performed. Moreover, this work has provided decisive evidence for the trustworthiness of AI in health care by presenting eight current state-of-the-art critical analyses regarding those more relevant research gaps. In addition, to the best of our knowledge, this study is the first to investigate the feasibility of utilising trustworthy and XAI applications in healthcare, by incorporating data fusion techniques and connecting various important pieces of information from available healthcare datasets and AI algorithms.The authors would like to acknowledge the support received through the following funding schemes of Australian Government: Australian Research Council (ARC) Industrial Transformation Training Centre (ITTC) for Joint Biomechanics under grant IC190100020. The authors also would like to acknowledge the support received through the QUT ECR SCHEME 2022 and the Centre for Data Science First Byte Scheme, The Queensland University of Technology

Intelligent triage method for early diagnosis autism spectrum disorder (ASD) based on integrated fuzzy multi-criteria decision-making methods

Background: Autism spectrum disorder (ASD) symptoms and severity levels vary from patient to patient, so treatment and healthcare will vary. However, little attention has been given to developing an autistic triage method for ASD patients concerning four issues: hybrid triage criteria, multi-selection criteria problems, criteria importance, and trade-off based on the inverse relationship between autistic criteria. Therefore, this study aims to develop a new method for triaging ASD patients and classifying them according to their severity of disorder using Fuzzy Multi-Criteria Decision Making (fMCDM) methods. Methods: Two methodology phases have been conducted: the first phase is to identify and preprocess the ASD dataset, including 988 autistic patients with 42 medical and Sociodemographic criteria. In the second phase, two fMCDM methods were used to develop the triage method. The fuzzy Delphi Method (FDM) is used to select the most influential criteria among the 42 based on thirteen psychologists in the psychological field. Then Fuzzy-Weighted Zero-Inconsistency (FWZIC) is used to assign weights to the important criteria according to four psychologists' opinions. Accordingly, the Processes for Triaging Autism Patients (PTAP) method has been developed for the first time for triaging and classifying patients into three severity levels: minor, moderate, and urgent. Results: For the preprocessed phase, 538 out of 988 patients were obtained as a new ASD dataset underwent data cleaning to capture only autism patients. For the second phase, the FDM results have selected 19 out of 42 criteria and can control the bias of psychologists' opinions, FWZIC has assigned the appropriate weights for the 19 criteria, and the PTAP method triages the 538 patients into three severity levels: 36 minor injuries, 432 moderate injuries, and 70 urgent injuries. More complex statistical analyses have been presented using MedCalc statistical software. Three physicians in the psychological field gave their subjective judgements for the diagnosis of 46 random samples of patients. The sensitivity results were 86.67%, 80%, and 90.91%, while the specificity results were 93.55%, 88.46%, and 94.29% for urgent, moderate, and minor levels, respectively. In addition, the accuracy was 91.30% for urgent, 84.78% for moderate, and 93.48% for minor. This assessment led to a deduction that a proposed ASD triage method can be applied with high performance. Conclusions: The developed triage method can be used for early autism diagnosis application and support clinical treatment utilizing the advantages of fMCDM techniques of multidimensional criteria. Four medical criteria were selected from the psychologists, while Sociodemographic acquired high attention with 15 selected criteria. For the correlation analysis of the 19 used criteria, the ‘Wave’ criterion has the highest correlation with the triage level and obtained 0.4523. On the contrary, the “Pointing with the index finger” criterion has the lowest correlation and obtained −0.0542. Limitations and future works have also been reported in this study. The study confirms the efficacy of the proposed triage method compared with previous studies in five comparative points with 100%

Real-time-based E-health systems: design and implementation of a lightweight key management protocol for securing sensitive information of patients

Group-based systems, such as e-health systems, have been introduced since the last few decades. E-health systems can be used anytime and anywhere for patient monitoring. Wireless networks are continuously used to monitor patients’ conditions and recovery progress. The confidentiality, integrity and authenticity of patients’ health records are important to secure in such applications. Efficient key management and distribution are required to secure e-health applications in a wireless mobile environment. However, existing key management protocols cannot route e-health applications securely due to the resource-constrained architecture of the wireless mobile environment. A novel and enhanced key management scheme which aims to identify the challenges related to the security and privacy issues of patients’ sensitive information through a strong encryption management is proposed in this study. The proposed model also aims to provide a well-organised and lightweight key management mechanism. This system requires few computations of keys and offers a null rekeying mechanism to ensure forward and backward secrecies. As a result, a secure and privacy-preserving key management scheme for e-health systems, which is known as the healthcare key management (HCKM) framework and aims to decrypt the ciphertext of the same plain text with different keys, is acquired. HCKM minimises the rekeying overhead of group members and the overhead expressed in terms of the number of exchanged messages whilst achieving a sufficiently high security level. The proposed protocol also can operate on dynamic scenarios with a large number (thousands) of nodes and exhibits an excellent performance under the assumption of low rate of evictions. © 2018, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature

Exploring decision-making techniques for evaluation and benchmarking of energy system integration frameworks for achieving a sustainable energy future

Energy Systems Integration (ESI) involves coordinating and planning energy systems to provide reliable and affordable energy services while minimizing environmental harm. It optimizes interactions among different energy sources to achieve sustainability goals and promotes efficient resource usage. However, evaluating and benchmarking ESI frameworks to select the most suitable and transparent ones is a complex Multi-Criteria Decision-Making (MCDM) problem. This complexity arises from trade-offs, conflicts, and importance considerations of the six ESI evaluation characteristics: Multidimensional, Multivectoral, Systemic, Futuristic, Systematic, and Applied. Hence, this study aims to address this complexity by integrating Fuzzy-Weighted Zero-Inconsistency (FWZIC) and Multi-Attributive Border Approximation Area Comparison (MABAC). The proposed methodology consists of two phases. Firstly, the development of a Dynamic Decision Matrix (DDM) to handle 26 ESI frameworks as alternatives and the six ESI characteristics criteria. Secondly, the integration of mathematical processes is formulated based on the FWZIC-MABAC methods. Using the FWZIC technique, the ESI evaluation criteria were weighted based on the preferences of twelve experts. ESI-C2 (Multivectoral) and ESI-C1 (Multidimensional) criteria received the highest weights of 0.195 and 0.190, respectively, while the ESI-C5 (Systematic) criterion received the lowest weight of 0.110. The remaining criteria, namely ESI-C3 (Systemic), ESI-C6 (Applied), and ESI-C4 (Futuristic) obtained weights of 0.189, 0.168, and 0.147, respectively. The MABAC benchmarking results showed that A11 (Energy Security) and A15 (Energy Security under decarbonization) ranked first with the highest score value of 0.28081 for both. Conversely, A19 (EJM) had the lowest score value of −0.17022. The systematic rank and sensitivity analysis assessments were conducted to verify the efficiency of the proposed methodology. We benchmarked the proposed methodology against three other benchmark studies and achieved a score of 100 % across three key perspectives. This methodology offers valuable support in making informed and sustainable decisions in the energy sector

A survey on communication components for IoT-based technologies in smart homes

The new and disruptive Internet of Things (IoT)-based technologies being used in smart homes have significantly bounded and dispersed communication components. To examine these technologies and provide researchers with a clear vision toward this area, we must be aware of the utilized approaches and the existing limitations in this line of research. To this end, an extensive search was conducted for articles dealing with (a) smart homes, (b) IoT, and (c) related applications were comprehensively reviewed and a coherent taxonomy for these articles was established. ScienceDirect, IEEE Xplore, and Web of Science databases were checked for articles on IoT-based smart home technologies. The retrieved articles were then filtered based on specified criteria “Communication components aspects”, and 82 articles were eventually selected and classified into four categories. The first category included articles that representing internet devices in a framework or model that follows the requirements of the stage in which any system is developed, the second category included analytical studies that monitor the possible changes in the variables used in a specific case study, the third category included evaluation, comparative studies, and assessing their worth or merit, and the fourth category included reviews and surveys a review and survey of the communication components of IoT-based smart home technologies. The motivation for using IoT-based technologies in smart homes, the issues related to application obstruction, and the development and utilization of smart homes are then examined based on the findings from the literature. With the exception of the 82 articles reviewed earlier, the telecommunication standards and concepts of this research were covering IoT solutions, communication protocols, IoT stack protocol, and quality of service for IoT based smart home technologies