Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud

Lakhan, Abdullah; Mastoi, Qurat-ul-ain; Elhoseny, Mohamed; Memon, Muhammad Suleman; Mohammed, Mazin Abed

doi:10.1080/17517575.2021.1883122

Cited by 77 publications

(62 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The gap-analysis highlights the aspects which studies did not consider in their IoMT application methodologies. Baresi et al [1] suggested a serverless-based IoMT system to minimize the resource cost of applications. The serverless system has a lower fog-cloud cost compared to virtual machines.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Lakhan

Mohammed

Rashid

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The Internet of Medical Things (IoMT) system is an emerging healthcare monitor paradigm that consists of devices, sensors, wireless network, and fog-cloud computing [1,2]. The sensors could be mobile devices, heartbeat sensors, blood pressure IoT sensors, ECG sensors, and many sensors connected with mobile devices [3].…”

Section: Introductionmentioning

confidence: 99%

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Lakhan

Mohammed

Rashid

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Tables 7 and 8 list the number of DWT and IDWT calls and their time ratio to the whole scheme, respectively. The time complexity of DWT is O M 2 L where M represents the size of the image and L represents the length of the filter [55][56][57][58][59][60]. The extraction process is the same as the embedding process that almost all of the time is spent on DWT and IDWT.…”

Section: Complexity Analysismentioning

confidence: 99%

A Reversible Watermarking System for Medical Color Images: Balancing Capacity, Imperceptibility, and Robustness

Zhou

Zhang

et al. 2021

Electronics

Self Cite

View full text Add to dashboard Cite

The authenticity and integrity of medical images in telemedicine has to be protected. Robust reversible watermarking (RRW) algorithms provide copyright protection and the original images can be recovered at the receiver’s end. However, the existing algorithms have limitations in their ability to balance the tradeoff among robustness, imperceptibility, and embedded capacity. Some of them are even not completely reversible. Besides, most medical image watermarking algorithms are not designed for color images. To improve their performance in protecting medical color image information, we propose a novel RRW scheme based on the discrete wavelet transform (DWT). First, the DWT provides a robust solution. Second, the modification of the wavelet domain coefficient guarantees the changes of integer values in the spatial domain and ensures the reversibility of the watermarking scheme. Third, the embedding scheme makes full use of the characteristics of the original image and watermarking. This reduces the modification of the original image and ensures better imperceptibility. Lastly, the selection of the Zernike moments order for geometric correction is optimized to predict attack parameters more accurately by using less information. This enhances the robustness of the proposed scheme against geometric attacks such as rotation and scaling. The proposed scheme is robust against common and geometric attacks and has a high embedding capacity without obvious distortion of the image. The paper contributes towards improving the security of medical images in remote healthcare.

show abstract

“…In the concept homework given in the video -lecture, not more than 20 minutes the video, a necessary required information to start the topics of study in class, helps students to understand, next day teacher may enquire about the viewed lecture and understandability of students as well in the class. Traditional flip model observed very effective flipping the classroom [36,37,38,72] .…”

Section: The Flipped Classroom May Be Classified In Three Types[4]: 1 Traditional Flipmentioning

confidence: 99%

A Review on State of the Art in Flipped Classroom Technology A Blended E-Learning

2021

IJETER

View full text Add to dashboard Cite

Flipped is an emerging mode of blended e-learning, as blended is mix mode of learning to enhance the skills of students, saving the time and cost either sides (students and educators). Flipped classroom is such as an e-learning blended approach in which instructions can be hovered into individual and groups, interactively to exchange and solving the assigned problems. Flipped classroom approach presents the dynamic, interactive and user’s friendly environments for in class and online blended learning. This kind of mode leads students to enhance their interpersonal skills, collaborate the experiences, and innovate new horizons by mutual activities for the given problems. The flipped mode has at least four dimensions; friendly easy environment, supporting culture for learning, contents availability when offline, minimizing time cost, trained educators/professionals and reducing repetition of learning content’s cost. In Flipped model of learning, first students go to seeking the exposure of problems by visiting the contents, materials, videos and other materials before getting in the classroom in person, fully aware to share or inquire the problem related matters in the class, its inverse approach against traditional institutional learning model. This is very effective mode of learningfor activity based and assignment-based learning strategies prior commencing class, fruitfulfeedback can be shared and reducing the tutorial part.

show abstract

Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud

Cited by 77 publications

References 23 publications

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

A Reversible Watermarking System for Medical Color Images: Balancing Capacity, Imperceptibility, and Robustness

A Review on State of the Art in Flipped Classroom Technology A Blended E-Learning

Contact Info

Product

Resources

About