List of Titles

A novel strategy to balance the results of cross-modal hashing

- Zhong, Fangming, Chen, Zhikui, Min, Geyong, Xia, Feng

Authors: Zhong, Fangming , Chen, Zhikui , Min, Geyong , Xia, Feng
Date: 2020
Type: Text , Journal article
Relation: Pattern Recognition Vol. 107, no. (2020), p.
Full Text: false
Reviewed:
Description: Hashing methods for cross-modal retrieval has drawn increasing research interests and has been widely studied in recent years due to the explosive growth of multimedia big data. However, a significant phenomenon which has been ignored is that there is a large gap between the results of cross-modal hashing in most cases. For example, the results of Text-to-Image frequently outperform that of Image-to-Text with a large margin. In this paper, we propose a strategy named semantic augmentation to improve and balance the results of cross-modal hashing. An intermediate semantic space is constructed to re-align the feature representations that embedded with weak semantic information. By using the intermediate semantic space, the semantic information of visual features can be further augmented before being sent to cross-modal hashing algorithms. Extensive experiments are carried out on four datasets via seven state-of-the-art cross-modal hashing methods. Compared against the results without semantic augmentation, the Image-to-Text results of these methods with semantic augmentation are improved considerably, which demonstrates the effectiveness of the proposed semantic augmentation strategy in bridging the gap between the results of cross-modal retrieval. Additional experiments are conducted on the real-valued, semi-supervised, semi-paired, partial-paired, and unpaired cross-modal retrieval methods, the results further indicates the effectiveness of our strategy in improving performance of cross-modal retrieval. © 2020 Elsevier Ltd

Quick View

Refining Parkinson’s neurological disorder identification through deep transfer learning

- Naseer, Amina, Rani, Monai, Naz, Saeeda, Razzak, Muhammad, Imran, Muhammad, Xu, Guandong

Authors: Naseer, Amina , Rani, Monai , Naz, Saeeda , Razzak, Muhammad , Imran, Muhammad , Xu, Guandong
Date: 2020
Type: Text , Journal article
Relation: Neural Computing and Applications Vol. 32, no. 3 (2020), p. 839-854
Full Text:
Reviewed:
Description: Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

Quick View

A novel fusion approach in the extraction of kernel descriptor with improved effectiveness and efficiency

- Karmakar, Priyabrata, Teng, Shyh, Lu, Guojun, Zhang, Dengsheng

Authors: Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun , Zhang, Dengsheng
Date: 2021
Type: Text , Journal article
Relation: Multimedia Tools and Applications Vol. 80, no. 10 (Apr 2021), p. 14545-14564
Full Text:
Reviewed:
Description: Image representation using feature descriptors is crucial. A number of histogram-based descriptors are widely used for this purpose. However, histogram-based descriptors have certain limitations and kernel descriptors (KDES) are proven to overcome them. Moreover, the combination of more than one KDES performs better than an individual KDES. Conventionally, KDES fusion is performed by concatenating them after the gradient, colour and shape descriptors have been extracted. This approach has limitations in regard to the efficiency as well as the effectiveness. In this paper, we propose a novel approach to fuse different image features before the descriptor extraction, resulting in a compact descriptor which is efficient and effective. In addition, we have investigated the effect on the proposed descriptor when texture-based features are fused along with the conventionally used features. Our proposed descriptor is examined on two publicly available image databases and shown to provide outstanding performances.

Quick View

Big data analytics for preventive medicine

- Razzak, Muhammad, Imran, Muhammad, Xu, Guandong

Authors: Razzak, Muhammad , Imran, Muhammad , Xu, Guandong
Date: 2020
Type: Text , Journal article
Relation: Neural Computing and Applications Vol. 32, no. 9 (2020), p. 4417-4451
Full Text:
Reviewed:
Description: Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

Quick View

Towards machine learning approach for digital-health intervention program

- Santhanagopalan, Meena, Chetty, Madhu, Foale, Cameron, Klein, Britt

Authors: Santhanagopalan, Meena , Chetty, Madhu , Foale, Cameron , Klein, Britt
Date: 2019
Type: Text , Journal article
Relation: Australian Journal of Intelligent Information Processing System Vol. 15, no. 4 (2019), p. 16-24
Full Text:
Reviewed:
Description: Digital-Health intervention (DHI) are used by health care providers to promote engagement within community. Effective assignment of participants into DHI programs helps increasing benefits from the most suitable intervention. A major challenge with the roll-out and implementation of DHI, is in assigning participants into different interventions. The use of biopsychosocial model [18] for this purpose is not wide spread, due to limited personalized interventions formed on evidence-based data-driven models. Machine learning has changed the way data extraction and interpretation works by involving automatic sets of generic methods that have replaced the traditional statistical techniques. In this paper, we propose to investigate relevance of machine learning for this purpose and is carried out by studying different non-linear classifiers and compare their prediction accuracy to evaluate their suitability. Further, as a novel contribution, real-life biopsychosocial features are used as input in this study. The results help in developing an appropriate predictive classication model to assign participants into the most suitable DHI. We analyze biopsychosocial data generated from a DHI program and study their feature characteristics using scatter plots. While scatter plots are unable to reveal the linear relationships in the data-set, the use of classifiers can successfully identify which features are suitable predictors of mental ill health.

CRT-BIoV : a cognitive radio technique for blockchain-enabled internet of vehicles

- Rathee, Geetanjali, Ahmad, Farhan, Kurugollu, Fatih, Azad, Muhammad, Iqbal, Razi, Imran, Muhammad

Authors: Rathee, Geetanjali , Ahmad, Farhan , Kurugollu, Fatih , Azad, Muhammad , Iqbal, Razi , Imran, Muhammad
Date: 2021
Type: Text , Journal article
Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 7 (2021), p. 4005-4015
Full Text: false
Reviewed:
Description: Cognitive Radio Network (CRN) is considered as a viable solution on Internet of Vehicle (IoV) where objects equipped with cognition make decisions intelligently through the understanding of both social and physical worlds. However, the spectrum availability and data sharing/transferring among vehicles are critical improving services and driving safety metrics where the presence of Malicious Devices (MD) further degrade the network performance. Recently, a blockchain technique in CRN-based IoV has been introduced to prevent data alteration from these MD and allowing the vehicles to track both legal and illegal activities in the network. In this paper, we provide the security to IoV during spectrum sensing and information transmission using CRN by sensing the channels through a decision-making technique known as Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), a technique that evokes the trust of its Cognitive Users (CU) by analyzing certain predefined attributes. Further, blockchain is maintained in the network to trace every activity of stored information. The proposed mechanism is validated rigorously against several security metrics using various spectrum sensing and security parameters against a baseline solution in IoV. Extensive simulations suggest that our proposed mechanism is approximately 70% more efficient in terms of malicious nodes identification and DoS threat against the baseline mechanism. © 2000-2011 IEEE.

Ranking station importance with human mobility patterns using subway network datasets

- Xia, Feng, Wang, Jinzhong, Kong, Xiangjie, Zhang, Da, Wang, Zhibo

Authors: Xia, Feng , Wang, Jinzhong , Kong, Xiangjie , Zhang, Da , Wang, Zhibo
Date: 2020
Type: Text , Journal article
Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 21, no. 7 (2020), p. 2840-2852
Full Text: false
Reviewed:
Description: Complex networks have become an active interdisciplinary field of research inspired by the empirical study of various networks. A subway network is a real-world example of complex networks in the transportation domain, which has attracted growing attention in network analysis recently. Analyzing human mobility patterns, specifically in ranking subway stations closely bounded by urban subway planning and individuals' travel experience, is still an open issue. In this paper, we propose a novel ranking method of station importance (SIRank) by utilizing human mobility patterns and improved PageRank algorithm. Specifically, by analyzing human mobility patterns of the subway system in Shanghai, we demonstrate both static and dynamic characteristics using two network models (Shanghai subway static network and Shanghai subway passenger network). In particular, the SIRank focuses on bi-directional passenger flow analysis between origins and destinations to iteratively generate the importance value for each station. We implement a range of the experiments to illustrate the effectiveness of SIRank using the real-world subway transaction datasets. The results demonstrate that the hit ratio in SIRank reaches 60% in the top five stations, which is much higher than that of ranking by a weighted mixed index (WMIRank) and ranking by node degree (NDRank) approaches. © 2000-2011 IEEE.

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
Reviewed:
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

A novel countermeasure technique for reactive jamming attack in internet of things

- Fadele, Alaba, Othman, Mazliza, Hashem, Ibrahim, Yaqoob, Ibrar, Imran, Muhammad, Shoaib, Muhammad

Authors: Fadele, Alaba , Othman, Mazliza , Hashem, Ibrahim , Yaqoob, Ibrar , Imran, Muhammad , Shoaib, Muhammad
Date: 2019
Type: Text , Journal article
Relation: Multimedia Tools and Applications Vol. 78, no. 21 (2019), p. 29899-29920
Full Text: false
Reviewed:
Description: In recent years, Internet of Things (IoT) has attracted significant attention because of its wide range of applications in various domains. However, security is a growing concern as users of small devices in an IoT network are unable to defend themselves against reactive jamming attacks. These attacks negatively affect the performance of devices and hinder IoT operations. To address such an issue, this paper presents a novel countermeasure detection and consistency algorithm (CDCA), which aims to fight reactive jamming attacks on IoT networks. The proposed CDCA uses a change in threshold value to detect and treat an attack. The algorithm employs channel signal strength to check packet consistency by determining if the data transmission value contradicts the threshold value. The node that sends the threshold value is periodically checked and the threshold value is compared with the current value after data transmission to find out if an attack has occurred in the network. Based on realistic simulation scenarios (e.g., with varying traffic interval, number of malicious nodes, and random mobility patterns), the performance of the proposed CDCA is evaluated using a Cooja simulator. Simulation results demonstrate the superiority of the proposed technique compared with contemporary schemes in terms of performance metrics such as energy consumption, traffic delay, and network throughput. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Quick View

The impact of environmental stochasticity on value-based multiobjective reinforcement learning

- Vamplew, Peter, Foale, Cameron, Dazeley, Richard

Authors: Vamplew, Peter , Foale, Cameron , Dazeley, Richard
Date: 2022
Type: Text , Journal article
Relation: Neural Computing and Applications Vol. 34, no. 3 (2022), p. 1783-1799
Full Text:
Reviewed:
Description: A common approach to address multiobjective problems using reinforcement learning methods is to extend model-free, value-based algorithms such as Q-learning to use a vector of Q-values in combination with an appropriate action selection mechanism that is often based on scalarisation. Most prior empirical evaluation of these approaches has focused on deterministic environments. This study examines the impact on stochasticity in rewards and state transitions on the behaviour of multi-objective Q-learning. It shows that the nature of the optimal solution depends on these environmental characteristics, and also on whether we desire to maximise the Expected Scalarised Return (ESR) or the Scalarised Expected Return (SER). We also identify a novel aim which may arise in some applications of maximising SER subject to satisfying constraints on the variation in return and show that this may require different solutions than ESR or conventional SER. The analysis of the interaction between environmental stochasticity and multi-objective Q-learning is supported by empirical evaluations on several simple multiobjective Markov Decision Processes with varying characteristics. This includes a demonstration of a novel approach to learning deterministic SER-optimal policies for environments with stochastic rewards. In addition, we report a previously unidentified issue with model-free, value-based approaches to multiobjective reinforcement learning in the context of environments with stochastic state transitions. Having highlighted the limitations of value-based model-free MORL methods, we discuss several alternative methods that may be more suitable for maximising SER in MOMDPs with stochastic transitions. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

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
Reviewed:
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

Showing items 1 - 12 of 12