Since the last decade, cloud-based electronic health records (EHRs) have gained significant attention to enable remote patient monitoring. The recent development of Healthcare 4.0 using the Internet of Things (IoT) components and cloud computing to access medical operations remotely has gained the researcher's attention from a smart city perspective. Healthcare 4.0 mainly consisted of periodic medical data sensing, aggregation, data transmission, data sharing, and data storage. The sensitive and personal data of patients lead to several challenges while protecting it from hackers. Therefore storing, accessing, and sharing the patient medical information on the cloud needs security attention that data should not be compromised by the authorized user's components of E-healthcare systems. To achieve secure medical data storage, sharing, and accessing in cloud service provider, several cryptography algorithms are designed so far. However, such conventional solutions failed to achieve the trade-off between the requirements of EHR security solutions such as computational efficiency, service side verification, user side verifications, without the trusted third party, and strong security. Blockchain-based security solutions gained significant attention in the recent past due to the ability to provide strong security for data storage and sharing with the minimum computation efforts. The blockchain made focused on bitcoin technology among the researchers. Utilizing the blockchain which secure healthcare records management has been of recent interest. This paper presents the systematic study of modern blockchain-based solutions for securing medical data with or without cloud computing. We implement and evaluate the different methods using blockchain in this paper. According to the research studies, the research gaps, challenges, and future roadmap are the outcomes of this paper that boost emerging Healthcare 4.0 technology. 

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Integration of Healthcare 4.0 and blockchain into secure cloud-based electronic health records systems

Deepfake network is a prominent topic of research as an application to various systems about security measures. Although there have been many recent advancements in facial reconstruction, the greatest challenge to overcome has been the means of finding an efficient and quick way to compute facial similarities or matches. This work is created utilizing the rationale-augmented convolutional neural network (CNN) on MATLAB R2019a platform using the Kaggle DeepFake Video dataset with an accuracy of 95.77%. Hence, real-time deepfake facial reconstruction for security purposes is difficult to complete concerning limited hardware and efficiency. This research paper looks into rational augmented CNN state-of-the-art technology utilized for deepfake facial reconstruction via hardware such as webcams and security cameras in real time. Additionally, discuss a history of face reconstruction and provide an overview of how it is accomplished.

Deepfake detection using rationale-augmented convolutional neural network

The emergence of the Industry 4.0 revolution to upgrade the Internet of Things (IoT) standards provides the prominence outcomes for the future wireless communication systems called 5G. The development of 5G green communication systems suffers from the various challenges to fulfill the requirement of higher user capacity, network speed, minimum cost, and reduced resource consumption. The use of 5G standards for Industry 4.0 applications will increase data rate performance and connected device's reliability. Since the arrival of novel Covid-19 disease, there is a higher demand for smart healthcare systems worldwide. However, designing the 5G communication systems has the research challenges like optimum resource utilization, mobility management, cost-efficiency, interference management, spectral efficiency, etc. The rapid development of Artificial Intelligence (AI) across the different formats brings performance enhancement compared to conventional techniques. Therefore, introducing the AI into 5G standards will optimize the performances further considering the various end-user applications. We first present the survey of the terms like 5G standard, Industry 4.0, and some recent works for future wireless communications. The purpose is to explore the current research problems using the 5G technology. We further propose the novel architecture for smart healthcare systems using the 5G and Industry 4.0 standards. We design and implement that proposed model using the Network Simulator (NS2) to investigate the current 5G methods. The simulation results show that current 5G methods for resource management and interference management suffer from the challenges like performance trade-offs. 

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5G standards for the Industry 4.0 enabled communication systems using artificial intelligence: perspective of smart healthcare system

We propose energy efficiency and quality-aware multi-hop one-way cooperative image transmission framework based on image pre-processing technique, wavelet-based two-dimensional discrete wavelet transform (2D-DWT) methodology, and decode-and-forward (DF) algorithm at relay nodes. The different cooperative communication methods that demonstrated their viability in various ways were reviewed. However, there are a few more issues that should be tended to while managing superb image transmission in WSNs, for example, extreme vitality utilization while preparing to proceed with image transmit, to achieve the broadcast between picture quality, and intensity of image transmitted. Before presenting the proposed model, this presents the review of recent and conventional techniques for cooperative image transmission.

The optimization efficient energy cooperative communication image transmission over WSN

A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.

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Stroke Risk Prediction with Machine Learning Techniques

Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.

/pdf/coronavirus-disease-covid-19-cases-analysis-using-machine-43fti7dfvz.pdf

Coronavirus disease (COVID-19) cases analysis using machine-learning applications.

Diagnostic imaging particularly image-guided biopsy is a necessary fundamental for making the diagnosis, performing stratification of risks, and planning the treatment of prostate cancers (PCa). The protocol of the American and European Urological Association confirms the systematic trans-rectal ultrasound (TRUS 10–12-core)-guided biopsy as the gold standard for primary diagnosis, but with growing knowledge and advanced imaging technologies; a multimodal approach of MRI/TRUS can improve the detection of PCa. This study aims to find and highlight the role of biomedical engineering development in imaging modalities for PCa detection. Eleven (11) articles used in this research, the imagine modalities that involved in all the studies were TRUS, MRI techniques, and CT/PET fusion methods. The results showed that most of the studies identified MRI-guided biopsy as superior to diagnosing PCa, compared to other modalities, and PSA level illustrated a significant decrease from (2008–2018), so that PSA test is strongly recommended for aged or patients with an enlarged prostate and or BPH. The correlation between age and PSA level also presented (0.43) which means that age is one of the factors that involved in raising PSA level. In conclusion, findings discovered that there has been tremendous improvement in PCa detection due to significant advancements in medical imaging software fusion engineering especially MRI techniques that enable better targeting of PCa. The rapid development in biomedical engineering specifically in imaging modalities and software fusion field.

Ameer Sardar Kwekha-Rashid

Papers

Coronavirus disease (COVID-19) cases analysis using machine-learning applications.

5G standards for the Industry 4.0 enabled communication systems using artificial intelligence: perspective of smart healthcare system

The impact of Advanced Imaging Software Fusion Techniques on The Detection of Prostate Cancer: A Review Paper