List of Titles

IoT-powered deep learning brain network for assisting quadriplegic people

- Vinoj, P., Jacob, Sunil, Menon, Varun, Balasubramanian, Venki, Piran, Md Jalil

Authors: Vinoj, P. , Jacob, Sunil , Menon, Varun , Balasubramanian, Venki , Piran, Md Jalil
Date: 2021
Type: Text , Journal article
Relation: Computers and Electrical Engineering Vol. 92, no. (2021), p.
Full Text: false
Reviewed:
Description: Brain-Computer Interface (BCI) systems have recently emerged as a prominent technology for assisting paralyzed people. Recovery from paralysis in most patients using the existing BCI-based assistive devices is hindered due to the lack of training and proper supervision. The system's continuous usage results in mental fatigue, owing to a higher user concentration required to execute the mental commands. Moreover, the false-positive rate and lack of constant control of the BCI systems result in user frustration. The proposed framework integrates BCI with a deep learning network in an efficient manner to reduce mental fatigue and frustration. The Deep learning Brain Network (DBN) recognizes the patient's intention for upper limb movement by a deep learning model based on the features extracted during training. DBN correlates and maps the different Electroencephalogram (EEG) patterns of healthy subjects with the identified pattern's upper limb movement. The stroke-affected muscles of the paralyzed are then activated using the obtained superior pattern. The implemented DBN consisting of four healthy subjects and a quadriplegic patient achieved 94% accuracy for various patient movement intentions. The results show that DBN is an excellent tool for providing rehabilitation, and it delivers sustained assistance, even in the absence of caregivers. © 2021

Blockchain for cloud exchange : a survey

- Xie, Shaoan, Zheng, Zibin, Chen, Weili, Wu, Jiajing, Dai, Hong-Ning, Imran, Muhammad

Authors: Xie, Shaoan , Zheng, Zibin , Chen, Weili , Wu, Jiajing , Dai, Hong-Ning , Imran, Muhammad
Date: 2020
Type: Text , Journal article
Relation: Computers and Electrical Engineering Vol. 81, no. (2020), p.
Full Text: false
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Description: Compared with single cloud service providers, cloud exchange provides users with lower price and flexible options. However, conventional cloud exchange markets are suffering from a number of challenges such as central architecture being vulnerable to malicious attacks and cheating behaviours of third-party auctioneers. The recent advances in blockchain technologies bring the opportunities to overcome the limitations of cloud exchange. However, the integration of blockchain with cloud exchange is still in infancy and extensive research efforts are needed to tackle a number of research challenges. To bridge this gap, this paper presents an overview on using blockchain for cloud exchange. In particular, we first give an overview on cloud exchange. We then briefly survey blockchain technology and discuss the issues on using blockchain for cloud exchange in aspects of security, privacy, reputation systems and transaction management. Finally, we present the open research issues in this promising area. © 2019

Cloud-assisted gamification for education and learning – recent advances and challenges

- Hakak, Saqib, Noor, Nurul, Ayub, Mohamad, Affal, Hannyzurra, Imran, Muhammad

Authors: Hakak, Saqib , Noor, Nurul , Ayub, Mohamad , Affal, Hannyzurra , Imran, Muhammad
Date: 2019
Type: Text , Journal article
Relation: Computers and Electrical Engineering Vol. 74, no. (2019), p. 22-34
Full Text: false
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Description: Gamification has gained considerable interest in education circles due to its capability of enhancing the learning process among students. In the future, it is expected that gamification will overtake the traditional way of learning resulting in issues such as scalability, upgradation of learning modules. To address these issues, merging gamification with cloud computing seems a viable solution. However, the employability of gamification through cloud computing is still in its infant stage. Hence, this article investigates the applicability of gamification through cloud computing and presents a comprehensive survey of state-of-the-art gamification in education and learning. We also identify the subject areas that can be gamified and taught using the cloud service. The critical elements and minimum requirements necessary to gamify education are also identified. Moreover, a specific cloud-assisted gamification architecture is proposed and discussed together with its possible applications. The article is concluded with the research challenges and suggestions for future work. © 2019 Elsevier Ltd. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**

Cloud robotics in smart manufacturing environments : challenges and countermeasures

- Yan, Hehua, Hua, Qingsong, Wang, Yingying, Wei, Wenguo, Imran, Muhammad

Authors: Yan, Hehua , Hua, Qingsong , Wang, Yingying , Wei, Wenguo , Imran, Muhammad
Date: 2017
Type: Text , Journal article
Relation: Computers and Electrical Engineering Vol. 63, no. (2017), p. 56-65
Full Text: false
Reviewed:
Description: In Smart Manufacturing Environments (SME), the use of cloud robotics is based on the integration of cloud computing and industrial robots, which provides a new technological approach to task execution and resource sharing compared to traditional industrial robots. However, research on cloud robotics in SME still faces some challenges. First, highly flexible load scheduling mechanisms are immature. Second, traditional optimization mechanisms for the network service quality do not meet the requirements of smart manufacturing due to time variability and service quality dynamics. And, finally, existing learning algorithms used without cloud-assisted resources cause great resource wasting. Accordingly, this paper explores main technologies related to cloud robotics in SME. The research contents include self-adaptive adjustment mechanisms for the service quality of a cloud robot network, computing load allocation mechanisms for cloud robotics, and group learning based on a cloud platform. The results presented in this paper are helpful to understand the internal mechanisms of perception and interaction, intelligent scheduling and control of cloud robot systems oriented to smart manufacturing, and the design of a cloud architecture oriented to group learning. © 2017

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