Multi-objectives reinforcement federated learning blockchain enabled Internet of things and Fog-Cloud infrastructure for transport data

For the past decade, there has been a significant increase in customer usage of public transport applications in smart cities. These applications rely on various services, such as communication and computation, provided by additional nodes within the smart city environment. However, these services are delivered by a diverse range of cloud computing-based servers that are widely spread and heterogeneous, leading to cybersecurity becoming a crucial challenge among these servers. Numerous machine-learning approaches have been proposed in the literature to address the cybersecurity challenges in heterogeneous transport applications within smart cities. However, the centralized security and scheduling strategies suggested so far have yet to produce optimal results for transport applications. This work aims to present a secure decentralized infrastructure for transporting data in fog cloud networks. This paper introduces Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB) for Transport Infrastructure. MORFLB aims to minimize processing and transfer delays while maximizing long-term rewards by identifying known and unknown attacks on remote sensing data in-vehicle applications. MORFLB incorporates multi-agent policies, proof-of-work hashing validation, and decentralized deep neural network training to achieve minimal processing and transfer delays. It comprises vehicle applications, decentralized fog, and cloud nodes based on blockchain reinforcement federated learning, which improves rewards through trial and error. The study formulates a combinatorial problem that minimizes and maximizes various factors for vehicle applications. The experimental results demonstrate that MORFLB effectively reduces processing and transfer delays while maximizing rewards compared to existing studies. It provides a promising solution to address the cybersecurity challenges in intelligent transport applications within smart cities. In conclusion, this paper presents MORFLB, a combination of different schemes that ensure the execution of transport data under their constraints and achieve optimal results with the suggested decentralized infrastructure based on blockchain technology.

Industrial Internet of Water Things architecture for data standarization based on blockchain and digital twin technology☆

INTRODUCTION

The Industrial Internet of Water Things (IIoWT) has recently emerged as a leading architecture for efficient water distribution in smart cities. Its primary purpose is to ensure high-quality drinking water for various institutions and households. However, existing IIoWT architecture has many challenges. One of the paramount challenges in achieving data standardization and data fusion across multiple monitoring institutions responsible for assessing water quality and quantity.

OBJECTIVE

This paper introduces the Industrial Internet of Water Things System for Data Standardization based on Blockchain and Digital Twin Technology. The main objective of this study is to design a new IIoWT architecture where data standardization, interoperability, and data security among different water institutions must be met.

METHODS

We devise the digital twin-enabled cross-platform environment using the Message Queuing Telemetry Transport (MQTT) protocol to achieve seamless interoperability in heterogeneous computing. In water management, we encounter different types of data from various sensors. Therefore, we propose a CNN-LSTM and blockchain data transactional (BCDT) scheme for processing valid data across different nodes.

RESULTS

Through simulation results, we demonstrate that the proposed IIoWT architecture significantly reduces processing time while improving the accuracy of data standardization within the water distribution management system.

CONCLUSION

Overall, this paper presents a comprehensive approach to tackle the challenges of data standardization and security in the IIoWT architecture.

SARS-CoV-2 Infection, Inflammation, Immunonutrition, and Pathogenesis of COVID-19

The COVID-19 pandemic, caused by the coronavirus, SARS-CoV-2, has claimed millions of lives worldwide in the past two years. Fatalities among the elderly with underlying cardiovascular disease, lung disease, and diabetes have particularly been high. A biometrics analysis on author's keywords was carried out, and searched for possible links between various coronavirus studies over the past 50 years, and integrated them. We found keywords like immune system, immunity, nutrition, malnutrition, micronutrients, exercise, inflammation, and hyperinflammation were highly related to each other. Based on these findings, we hypothesized that the human immune system is a multilevel super complex system, which employs multiple strategies to contain microorganism infections and restore homeostasis. It was also found that the behavior of the immune system is not able to be described by a single immunological theory. However, one main strategy is "self-destroy and rebuild", which consists of a series of inflammatory responses: 1) active self-destruction of damaged/dysfunctional somatic cells; 2) removal of debris and cells; 3) rebuilding tissues. Thus, invading microorganisms' clearance could be only a passive bystander response to this destroy-rebuild process. Microbial infections could be self-limiting and promoted as an indispensable essential nutrition for the vast number of genes existing in the microorganisms. The transient nutrition surge resulting from the degradation of the self-destroyed cell debris coupled with the existing nutrition state in the patient may play an important role in the pathogenesis of COVID-19. Finally, a possible coping strategies to mitigate COVID-19, including vaccination is also discussed.

A Literature Review of E-Learning Technology in Higher Education

The increase in the use of E-learning during the COVID-19 pandemic as a consequence of the call for Work From Home (WFH) is increasingly massive. In contrast to regular times, E-learning during the COVID-19 pandemic depends on available home resources. Technology plays an essential role in supporting the implementation of e-learning which must consider the resources available at home. This research is a systematic literature review using database sources from ScienceDirect and Emerald. The inclusion criteria are research that discusses e-learning in higher education during the COVID-19 pandemic (2019-2020) from a computer science perspective. An exclusion criterion is research examining e-learning in higher education from sources outside ScienceDirect. The search results from ScienceDirect obtained 518, and Emerald got 66 relevant articles. The next stage is reviewing abstracts and titles that focus on technology in e-learning on the lecturer and student side; We obtained 23 articles and then checked them in full text. This literature study shows that technology as a support for e-learning systems during the COVID-19 period in universities needs to consider the resources available at home, namely infrastructure and applications for students and teachers, for future work in the research of friendly e-learning technology to the availability of resources at home.

A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models

Biomarkers including fasting blood sugar, heart rate, electrocardiogram (ECG), blood pressure, etc. are essential in the heart disease (HD) diagnosing. Using wearable sensors, these measures are collected and applied as inputs to a deep learning (DL) model for HD diagnosis. However, it is observed that model accuracy weakens when the data gathered are scarce or imbalanced. Therefore, this work proposes two DL-based frameworks, GAN-1D-CNN, and GAN-Bi-LSTM. These frameworks contain: (1) a generative adversarial network (GAN) and (2) a one-dimensional convolutional neural network (1D-CNN) or bi-directional long short-term memory (Bi-LSTM). The GAN model is utilized to augment the small and imbalanced dataset, which is the Cleveland dataset. The 1D-CNN and Bi-LSTM models are then trained using the enlarged dataset to diagnose HD. Unlike previous works, the proposed frameworks increase the dataset first to avoid the prediction bias caused by the limited data. The GAN-1D-CNN achieved 99.1% accuracy, specificity, sensitivity, F1-score, and 100% area under the curve (AUC). Similarly, the GAN-Bi-LSTM obtained 99.3% accuracy, 99.2% specificity, 99.3% sensitivity, 99.2% F1-score, and 100% AUC. Furthermore, time complexity of proposed frameworks is investigated with and without principal component analysis (PCA). The PCA method reduced prediction times for 61 samples using GAN-1D-CNN and GAN-Bi-LSTM to 68.8 and 74.8 ms, respectively. These results show that it is reliable to use our frameworks for augmenting limited data and predicting heart disease.

Critical factors that affecting adoption of e-learning based on cloud computing for Iraqi higher education organizations

Cloud computing-based e-learning is one of the most popular techniques discovered to promote traditional instruction by software applications and a virtual learning environment. There are many factors that could be regarded in a company's effort to implement or embrace new technology initiatives.  As in the literature, many scientists and researchers have particularly explored the factors that influence the adoption of cloud computing along with e-learning issues based on cloud computing that are derived from standards models such as Technology-Organization-Environment (TOE) framework and Technology Acceptance Model (TAM). The aim of the study is to identify critical factors that impact the adoption of cloud computing-based e-learning for Iraqi higher education organizations. Indeed, we identified the 14 critical factors that affect cloud computing uptake for e-learning system in higher education organizations such as compatibility, complexity, cost saving, security and privacy, integrity, and etc. This research examines the ideas and literature used to examine adoption of cloud computing to define future variables that may influence the adoption of cloud computing.

A Review on The Theories of Adoption Telemedicine in Middle Ease : Toward Building Iraqi Telemedicine

Telemedicine is the use of modern telecommunications and information technologies for the provision of clinical care to individuals at a distance, and transmission of information to provide that care. Recent advances in information technology and biomedicine increased significantly the technical feasibility, clinical feasibility and affordability enabled collaborative of telemedicine and medical service delivery. Health organizations around the world are becoming more interested in the acquisition and implementation the telemedicine technology to improve or expand existing services and patient care. The ultimate success of telemedicine in an organization requires the adoption of adequate care both technological and managerial issues. This study examined theadoption theories,the key management problem facing many health care organizations which interested in or currently evaluating telemedicine . this research models targeted the technology adoption and empirically evaluated in a study of the investigation involving more public health agencies and criticize the theories.Several research and management implications that emerged from the study results are discussed.

E-learning Service Framework Implementation for COVID-19 pandemic (Case study Informatic dept. Universitas Muhammadiyah Magelang)

Work from home is a way to reduce the development of the coronavirus termed COVID-19. In the world of education, we have been familiar with e-learning systems where learning is done with any time, any ware, anybody, and anyplace. Nevertheless, the readiness for implementation is not relaxed because there are technical and non-technical obstacles. There are four roles in e-learning wherein the role of instructor and student's role is the core, but the readiness of infrastructure and integration between systems is a problem in itself.  The consequence of Work from home is to provide resources independently at home, with cost and geographical challenges. The development of E-learning 3.0, with its U-learning concept, gives a different dimension, especially in the use of cloud service provider applications. In this paper, the E-learning Service Framework is proposed by considering the results of a review of the LearningCube modeling, Interactive Mode Architecture, and Critical Success Factors.

Multi-Agent Systems in Fog-Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring

In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.

Exploring the contributing factors to workarounds to the hospital information system in Malaysian hospitals

OBJECTIVES

The objective of this study was to identify the factors influencing workarounds to the Hospital Information System (HIS) in Malaysian government hospitals.

METHODS

Semi-structured interviews were conducted among 31 medical doctors in three Malaysian government hospitals on the implementation of the Total Hospital Information System (THIS) between March and May 2015. A thematic qualitative analysis was performed on the resultant data to deduce the relevant themes.

RESULTS

Five themes emerged as the factors influencing workarounds to the HIS: (a) typing skills, (b) system usability, (c) computer resources, (d) workload, and (e) time.

CONCLUSIONS

This study provided the key factors as to why doctors were involved in workarounds during the implementation of the HIS. It is important to understand these factors in order to help mitigate work practices that can pose a threat to patient safety.

MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management

In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks’ priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.

The Synchronisation Of Motion Capture Results In The Animation Character Reinforcement Process

Animation is a collection of frames that express a motion activity. The animation consists of actors, characters and performance components that present a story — one of the technologies in making an animation with motion capture. The results of catching motion in the form of motion record data are then synchronised to animated characters. However, the problem is that there are difficulties in synchronising animated characters with the results of catching motion so that the motion can be more subtle. On capture, this motion uses Kinect as a motion capture sensor for actors. The results of this capture then become motion data. The synchronisation of motion data from the motion capture results with Kinect then adjusts the joint body character points, frames, time with three-dimensional (3D) animated characters according to the flow of character actors. This synchronisation is adjusted to the recording of character movements starting from the motion of humans, animals and objects that invite motion activity with motion capture methods both marked and without markers. In making 3D, there are models of giving a body or bone frame (rigging) after giving the body frames is done by giving visual effects, lighting, rendering, and compositing. Rigging is the installation of bones to characters used to place and manipulate animated controls on characters that will be animated to produce the desired gesture. Animation synchronisation is to create animated characters manually; the file is used for. The synchronisation is Biovision Hierarchy (BVH) with gesture giving movement without having to save the keyboard in every animation movement manually.

THE DESIGN OF MOBILE PERSONALISED HEALTH RECORDS

Pervasive Electronic Health Record provides important medical history record for an individual within a healthcare system. By using pervasive storage devices, patient’s health records can be accessed electronically by authorized healthcare providers and healthcare professionals in the right place at the right time. However, the research found that not all healthcare practitioners are ready to adopt this approach and mobile technology into their daily work while providing healthcare services to the patients. This is important indicators to be acquired before implementing the technology in the real environment of healthcare.  The aim for this paper is to identify the crucial clinical dataset to be captured and viewed by the healthcare professionals and important design components for mobile personalized health records. The input will indicate that the healthcare professional tend to use the technology. In addition, the collected datasets could be used as input to design mobile personalized health record application.

Willingness to Adopt Telemedicine in Major Iraqi Hospitals: A Pilot Study

The Iraqi healthcare services are struggling to regain their lost momentum. Many physicians and nurses left Iraq because of the current situation in the country. Despite plans of calling back the skilled health workforce, they are still worried by the disadvantages of their return. Hence, technology plays a central role in taking advantage of their profession through the use of telemedicine. Studying the factors that affect the implementation of telemedicine is necessary. Telemedicine covers network services, policy makers, and patient understanding. A framework that includes the influencing factors in adopting telemedicine in Iraq was developed in this study. A questionnaire was distributed among physicians in Baghdad Medical City to examine the hypothesis on each factor. The Statistical Package for the Social Sciences was utilized to verify the reliability of the questionnaire and Cronbach's alpha test shows that the factors have values more than 0.7, which are standard.

Electronic health records approaches and challenges: a comparison between Malaysia and four East Asian countries

An integrated Lifetime Health Record (LHR) is fundamental for achieving seamless and continuous access to patient medical information and for the continuum of care. However, the aim has not yet been fully realised. The efforts are actively progressing around the globe. Every stage of the development of the LHR initiatives had presented peculiar challenges. The best lessons in life are those of someone else's experiences. This paper presents an overview of the development approaches undertaken by four East Asian countries in implementing a national Electronic Health Record (EHR) in the public health system. The major challenges elicited from the review including integration efforts, process reengineering, funding, people, and law and regulation will be presented, compared, discussed and used as lessons learned for the further development of the Malaysian integrated LHR.